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								Grad. Inst. of Networking &amp; Multimedia, Nat. Taiwan Univ., Taipei, Taiwan 
								
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								Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada 
								
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								Sch. of Software &amp; Microelectron., Peking Univ., Beijing, China 
								
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								Beijing Key Lab. of Intell. Telecommun. Software &amp; Multimedia, Beijing Univ. of Posts &amp; Telecommun., Beijing, China 
								
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								Dept. of Comput. Sci., Univ. of California, Davis, CA, USA 
								
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								Dept. of Inf. Manage., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan 
								
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								Coll. of Inf. Sci. &amp; Technol., Nanjing Univ. of Aeronaut. &amp; Astronaut., Nanjing, China 
								
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								Sch. of Comput. Sci. &amp; Inf., Univ. Coll. Dublin, Dublin, Ireland 
								
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										Predicting Customer Demographics in a Mobile Social Network
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						Aarthi, S.; Bharanidharan, S.; Saravanan, M.; Anand, V. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.13" target="blank">
									10.1109/ASONAM.2011.13
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										A network made of individuals connected based on their communication behaviour using mobile phones can be called as a Mobile Social Network. Mapping <span class="snippet">and</span> measuring of interactions <span class="snippet">and</span> flows between people across mobile social networks are being performed extensively in an attempt to understand the intriguing patterns of human behaviour. Such analyses can help in arriving at useful inferences for improving the accuracy of 'Targeted Advertisements'.This paper makes one such attempt to extract the demographics (i.e. age, gender <span class="snippet">and</span> economic status) of a person based on his/her connectivity in his/her respective social network(s) <span class="snippet">and</span> mobile phone usage over a period of time. The need for prediction arises from the fact that, for prepaid users, the demographics are either unavailable or inaccurate. The results produced are evaluated <span class="snippet">and</span> standardized based on proven statistics pertaining to the nation considered. We use candlestick charts to compare the experimental results.
										
						                	
						                	
						                	
						                			
						             				
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										Trust Based Recommendation System with <span class="snippet">Social Network Analysis</span>
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						Lixin Zhou 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513">
			                	<br>Information Engineering <span class="snippet">and</span> Computer Science, 2009. ICIECS 2009. International Conference on
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ICIECS.2009.5362789" target="blank">
									10.1109/ICIECS.2009.5362789
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						 <br>Publication Year: 2009
							
										
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										Trust can significantly enhance the utility of a recommendation system. <span class="snippet">Social Network Analysis</span> can find key person <span class="snippet">and</span> very useful relations between customers. In this paper, at first a trust relation network is established using natural language processing technique <span class="snippet">and</span> <span class="snippet">social network analysis</span>. Then trust based recommendation algorithms are put forward <span class="snippet">and</span> discussed.
										
						                	
						                	
						                	
						                			
						             				
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										Facilitating understanding of team-based adaptation policies
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						Hussain, S.S.; Veiel, D.; Haake, J.M.; Lukosch, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5755590">
			                	<br>Collaborative Computing: Networking, Applications <span class="snippet">and</span> Worksharing (CollaborateCom), 2010 6th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										Knowledge work in modern organizations is increasingly performed collaboratively by distributed teams. Such collaboration involves dynamically changing situations. Making the collaboration environment fit the needs of these situations is supported by system-initiated adaptation based on the user <span class="snippet">and</span> team context. Adaptation in the collaboration environment may confuse the users, because they cannot remember all adaptation policies. In this paper we propose an approach to present context enriched explanations to help them understand the adaptation behavior. Also, we describe a <span class="snippet">social network analysis</span> strategy to identify socially related <span class="snippet">and</span> current situation relevant communication partners. Integrated on demand communication is facilitated among these partners for discussion <span class="snippet">and</span> understanding the adaptation policies.
										
						                	
						                	
						                	
						                			
						             				
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										Examining social networks between educational institutions, industrial partners, <span class="snippet">and</span> the Canadian government
									</a>
								
							
						
					
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						Yau, C.; Straight, M.; Bir, R.; Addam, O.; Shafiq, M.O.; Alhajj, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6294629">
			                	<br>Information Reuse <span class="snippet">and</span> Integration (IRI), 2012 IEEE 13th International Conference on
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								Bioengineering</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/IRI.2012.6303073" target="blank">
									10.1109/IRI.2012.6303073
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						 <br>Publication Year: 2012
							
										
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										Between 2000 <span class="snippet">and</span> 2010, the Canadian federal government disbursed $716 893 740 in research grants through the Natural Sciences <span class="snippet">and</span> Engineering Research Council of Canada (NSERC)'s Research Partnerships Programs. This is only one branch, of one agency, in the immense bureaucracy of government funding. This study will examine the social networks which resulted from these research grants. Over this eleven years period, 6112 individual grants were awarded through these programs, involving 187 educational institutions <span class="snippet">and</span> 4231 industrial partners. We used <span class="snippet">social network analysis</span> (SNA) to explore these networks. We employed centrality measures to identify key entities in the networks <span class="snippet">and</span> visualization tools to provide a “big-picture” view of the network topology. Specifically, this study will examine changing trends in the leading educational institutions, industrial partners, <span class="snippet">and</span> areas of research over the last decade. While the limited scope of our study can only manage to scratch the surface of such a large network, we aim to provide a broad over-view of the problem <span class="snippet">and</span>, hopefully, to demonstrate the importance of, <span class="snippet">and</span> to inspire, future explorations in this complex field.
										
						                	
						                	
						                	
						                			
						             				
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										Bridging the Knowledge Gap: Using Social Network Methodology for Detecting, Connecting <span class="snippet">and</span> Facilitating Informal Networked Learning in Organizations
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						de Laat, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5716643">
			                	<br>System Sciences (HICSS), 2011 44th Hawaii International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/HICSS.2011.88" target="blank">
									10.1109/HICSS.2011.88
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										This paper presents a <span class="snippet">social network analysis</span> (SNA) based methodology for holding up a mirror within organisations to detect <span class="snippet">and</span> visualise informal networked learning activities spontaneously undertaken by its workers. Practice driven informal workplace learning often goes undetected, but is increasingly seen as a powerful way of optimizing organizational learning <span class="snippet">and</span> knowledge management practices based on meaningful day-to-day learning interactions. Empirical data from this study shows that using the Network mirror helps to bridge latent <span class="snippet">and</span> weak ties between workers in organisations who are dealing with similar challenges <span class="snippet">and</span> questions. The methodology combines SNA with group decision software not only to detect networks but also to make explicit what these networks are working on <span class="snippet">and</span> what is meaningful to them. Triangulation of these data streams leads to new interventions driven by bottom-up networked activity as opposed to management directives.
										
						                	
						                	
						                	
						                			
						             				
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										Predicting the Nexus between Post-Secondary Education Affordability <span class="snippet">and</span> Student Success: An Application of Network-Based Approaches
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						Arulselvan, A.; Mendoza, P.; Boginski, V.; Pardalos, P. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.82" target="blank">
									10.1109/ASONAM.2009.82
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										The cost of post-secondary education in the U.S. continues to grow faster than salaries <span class="snippet">and</span> inflation. In fact, the real cost of a college education has climbed almost 30 in the past 10 years <span class="snippet">and</span> shows no sign of stabilizing in the near future. The economic competitiveness of the country increasingly depends on a skilled workforce with a post-secondary education capable of dealing with the demands of the global market. Thus, college attainment is at the center of producing a skilled workforce, <span class="snippet">and</span> so it is, post-secondary education affordability. Using the national post-secondary student aid surveyor the year 2003- 2004, which is representative of the entire undergraduate population in the U.S., this study examines the various ways students <span class="snippet">and</span> families pay for post-secondary education <span class="snippet">and</span> its subsequent effect on persistence <span class="snippet">and</span> performance for all groups of students across racial/ethnic <span class="snippet">and</span> social-economic status lines.We use a spectral clustering algorithm based on normalized cuts to classify students based on their similarities. More specifically, we construct a social network with the students as the nodes of the graph <span class="snippet">and</span> edge between pair of the students is weighted based on their similarity in attributes. We then obtain three nontrivial smallest of the Laplacian matrix. We use these to perform a k-means clustering in the eigenspace. We were able to establish meaningful clusters by this approach that helps in classifying students based on the relation between their persistence level <span class="snippet">and</span> conditions of living.
										
						                	
						                	
						                	
						                			
						             				
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										Interconnectedness of Complex Systems of Internet of Things through <span class="snippet">Social Network Analysis</span> for Disaster Management
									</a>
								
							
						
					
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						Zelenkauskaite, A.; Bessis, N.; Sotiriadis, S.; Asimakopoulou, E. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6336635">
			                	<br>Intelligent Networking <span class="snippet">and</span> Collaborative Systems (INCoS), 2012 4th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/iNCoS.2012.25" target="blank">
									10.1109/iNCoS.2012.25
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						 <br>Publication Year: 2012
							
										
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										This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects <span class="snippet">and</span> their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation <span class="snippet">and</span> analysis. Within this in mind, our focus is on the area of complex social networks <span class="snippet">and</span> the dynamic social network construction within the context of IoT. This is by highlighting <span class="snippet">and</span> addressing the tagging issues of the objects to the real-world domain such as in disaster management, these are in relation to their hierarchies <span class="snippet">and</span> interrelation within the context of <span class="snippet">social network analysis</span>. Specifically, we suggest to investigate <span class="snippet">and</span> deepen the understanding of the IoT paradigm through the application of <span class="snippet">social network analysis</span> as a method for interlinking objects -- <span class="snippet">and</span> thus, propose ways in which IoT could be subsequently interlinked <span class="snippet">and</span> analyzed through <span class="snippet">social network analysis</span> approach - which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications <span class="snippet">and</span> key characteristics of disaster management <span class="snippet">and</span> the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain.
										
						                	
						                	
						                	
						                			
						             				
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										Mining social networks of Thai researchers in IT-related areas
									</a>
								
							
						
					
				</h3>
				
					
						Tangsripairoj, S.; Sripirom, U. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5876042">
			                	<br>Computer Science <span class="snippet">and</span> Software Engineering (JCSSE), 2011 Eighth International Joint Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/JCSSE.2011.5930151" target="blank">
									10.1109/JCSSE.2011.5930151
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						 <br>Publication Year: 2011
							
										
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									<p>
										This research focuses on the study of social networks of Thai researchers that conducted research in the computer science <span class="snippet">and</span> information technology fields <span class="snippet">and</span> submitted their works to two conferences which are the International Joint Conference on Computer Science <span class="snippet">and</span> Software Engineering (JCSSE) <span class="snippet">and</span> the National Computer Science <span class="snippet">and</span> Engineering Conference (NCSEC) between 2005 <span class="snippet">and</span> 2008. The bibliographical data which appeared in their research was extracted for database construction. The principle of <span class="snippet">Social Network Analysis</span> was applied for data analysis in order to find relationships between researchers, research topic categories, conferences <span class="snippet">and</span> years in which they were organized. The data analysis enables users to know which researchers in Thailand conducted their research in what area, to which academic institutes they were attached, <span class="snippet">and</span> who were their co-authors. In addition, the analysis result also allows users to see trends of research topics of Thai researchers. It was hoped that as a result of this analysis, the derived database will be useful for people who wish to search for research data in areas in which they are interested. Similarly, government agencies working on the promotion <span class="snippet">and</span> support of research work will be informed of research trends in Thailand so as to speed up work in support of conducting research in significant areas or those in which the country is lacking.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5730298&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Community detection via semi-synchronous label propagation algorithms
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				</h3>
				
					
						Cordasco, G.; Gargano, L. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5724309">
			                	<br>Business Applications of <span class="snippet">Social Network Analysis</span> (BASNA), 2010 IEEE International Workshop on
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					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/BASNA.2010.5730298" target="blank">
									10.1109/BASNA.2010.5730298
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						 <br>Publication Year: 2010
							
										
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										A recently introduced novel community detection strategy is based on a label propagation algorithm (LPA) which uses the diffusion of information in the network to identify communities. Studies of LPAs showed that the strategy is effective in finding a good community structure. Label propagation step can be performed in parallel on all nodes (synchronous model) or sequentially (asynchronous model); both models present some drawback, e.g., algorithm termination is nor granted in the first case, performances can be worst in the second case. In this paper, we present a semi-synchronous version of LPA which aims to combine the advantages of both synchronous <span class="snippet">and</span> asynchronous models. We prove that our models always converge to a stable labeling. Moreover, we experimentally investigate the effectiveness of the proposed strategy comparing its performance with the asynchronous model both in terms of quality, efficiency <span class="snippet">and</span> stability. Tests show that the proposed protocol does not harm the quality of the partitioning. Moreover it is quite efficient; each propagation step is extremely parallelizable <span class="snippet">and</span> it is more stable than the asynchronous model, thanks to the fact that only a small amount of randomization is used by our proposal.
										
						                	
						                	
						                	
						                			
						             				
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							[Copyright notice]
						
						
					
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			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6093899">
			                	<br>Visual Analytics Science <span class="snippet">and</span> Technology (VAST), 2011 IEEE Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/VAST.2011.6102424" target="blank">
									10.1109/VAST.2011.6102424
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Towards automatic extraction of social networks of organizations in PubMed abstracts
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						Jonnalagadda, S.; Topham, P.; Gonzalez, G. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5314287">
			                	<br>Bioinformatics <span class="snippet">and</span> Biomedicine Workshop, 2009. BIBMW 2009. IEEE International Conference on
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								<a href="http://dx.doi.org/10.1109/BIBMW.2009.5332108" target="blank">
									10.1109/BIBMW.2009.5332108
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						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										<span class="snippet">Social network analysis</span> (SNA) of organizations can attract great interest from government agencies <span class="snippet">and</span> scientists for its ability to boost translational research <span class="snippet">and</span> accelerate the process of converting research to care. For SNA of a particular disease area, we need to identify the key research groups in that area by mining the affiliation information from PubMed. This not only involves recognizing the organization names in the affiliation string, but also resolving ambiguities to identify the article with a unique organization. We present here a process of normalization that involves clustering based on local sequence alignment metrics <span class="snippet">and</span> local learning based on finding connected components. We demonstrate the application of the method by analyzing organizations involved in angiogenensis treatment, <span class="snippet">and</span> demonstrating the utility of the results for researchers in the pharmaceutical <span class="snippet">and</span> biotechnology industries or national funding agencies.
										
						                	
						                	
						                	
						                			
						             				
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										An Integrated Home Financial Investment Learning Environment Applying Cloud Computing in <span class="snippet">Social Network Analysis</span>
									</a>
								
							
						
					
				</h3>
				
					
						Mao-Ping Wen; Hsio-Yi Lin; An-Pin Chen; Chyan Yang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ASONAM.2011.97" target="blank">
									10.1109/ASONAM.2011.97
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										This paper tried to apply cloud computing technology in <span class="snippet">social network analysis</span> for a comprehensive home financial learning environment that individual investors may use as a reference in establishing web-based learning <span class="snippet">and</span> investment platforms. The major contributions were described in three parts. First, this paper advanced the <span class="snippet">social network analysis</span> technology to be able to handle millions of nodes <span class="snippet">and</span> links. Second, we demonstrate how cloud computing can be applied to advanced computing in social network. Third, we performed several intelligent analyses on a very popular social network, IHFILE, to identify some interesting <span class="snippet">and</span> important features of it. In addition to analyzing a homogeneous social network such as IHFILE, we also propose direction of how cloud computing can be performed on a <span class="snippet">social network analysis</span> as our future work.
										
						                	
						                	
						                	
						                			
						             				
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			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.1" target="blank">
									10.1109/ASONAM.2010.1
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										Groupsona: A Method for Discovering the Needs of Social Interaction Embedded Services
									</a>
								
							
						
					
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						Jyun-Cheng Wang; Cheng-Hsin Chiang; Ching-Hui Chang; Pei-Wen Chung 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5716643">
			                	<br>System Sciences (HICSS), 2011 44th Hawaii International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/HICSS.2011.217" target="blank">
									10.1109/HICSS.2011.217
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Service design determines the success of a business operation. How to identify customers' needs <span class="snippet">and</span> design high-quality services are important issues. Persona has been adopted as a tool for identifying needs of individual users for service design. Its focus on individual needs reveals its limitations in failing to capture the interactions among individuals. This study proposes a new user-needs assessment method, named Groupsona, to identify individual customer's social needs of interacting with other members in the group. Groupsonas are group archetypes that represent customers' needs based on the group's perspectives <span class="snippet">and</span> the interactive behaviors of users in the group. Based on concepts of constructing egocentric network in <span class="snippet">Social Network Analysis</span>, Groupsona provides a tool for recognizing network patterns <span class="snippet">and</span> understanding the various ways of group interactions. We applied Groupsona to analyze weight loss group members' needs. The analytical results showed Groupsona can represent <span class="snippet">and</span> identify the characteristics of customers' needs.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5428693&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Tool for the Analysis of Social Networks in Collaborative Software Development
									</a>
								
							
						
					
				</h3>
				
					
						Schwind, M.; Schenk, A.; Schneider, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5428222">
			                	<br>System Sciences (HICSS), 2010 43rd Hawaii International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/HICSS.2010.40" target="blank">
									10.1109/HICSS.2010.40
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										In this article, we present a tool that is designed to investigate the collaboration network between actors in software development groups. Our approach uses data derived from the version control system SVN (subversion) to retrieve the collaboration structures using an elaborate refinement process. The software `SVN Network Analysis Tool' (SVNNAT) aims to reveal the strengths <span class="snippet">and</span> properties of collaboration ties between the developers in order to analyze their productivity <span class="snippet">and</span> their quality of work in a given constellation of a software development network. In contrast to an earlier version of SVNNAT, our current approach accounts for the specific structures of programming languages in software code to separate technical artifacts from the information about the actors' collaboration in the development network. The result of the evaluation process is a social network of software developers that can be analyzed using typical indicators of topology properties like betweenness, closeness, <span class="snippet">and</span> degree centralization. In a further step such network analysis can be used to propose an efficient network structure for developers of newly designed software projects.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6149423&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Network-Based Business Process Analysis
									</a>
								
							
						
					
				</h3>
				
					
						Levina, O.; Hillmann, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6148328">
			                	<br>System Science (HICSS), 2012 45th Hawaii International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/HICSS.2012.447" target="blank">
									10.1109/HICSS.2012.447
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										This paper presents a quantitative approach to business process analysis using metrics from (social) network analysis. It is claimed that using this approach not only allows quantification of business process properties, but also provides insights on business process behavior that is not obvious from business process descriptions or models. The goal is to show that process characteristics can be explored <span class="snippet">and</span> analyzed using network theory <span class="snippet">and</span> methods of statistical analysis. The focus of this work is put on process type definition. To validate the approach business process model networks are evaluated against artificial networks with selected random graph distributions. The identified metrics are evaluated using a sample of real-life business processes.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6406375&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Talking Places: Modelling <span class="snippet">and</span> Analysing Linguistic Content in Foursquare
									</a>
								
							
						
					
				</h3>
				
					
						Bauer, S.; Noulas, A.; Seaghdha, D.O.; Clark, S.; Mascolo, C. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6403618">
			                	<br>Privacy, Security, Risk <span class="snippet">and</span> Trust (PASSAT), 2012 International Conference on <span class="snippet">and</span> 2012 International Confernece on Social Computing (SocialCom)
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.107" target="blank">
									10.1109/SocialCom-PASSAT.2012.107
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										The advent of <span class="snippet">online</span> social media <span class="snippet">and</span> the growing popularity of sensor-equipped mobile devices have created a vast landscape of location-aware applications <span class="snippet">and</span> services. This goldmine of data, including temporal <span class="snippet">and</span> spatial information of unprecedented granularity, can help researchers gain insights into the behavioural patterns of people at a global scale. Here we analyse the textual content of millions of comments published alongside Foursquare user check-ins. For this, we extend a standard topic modelling approach so that it explicitly takes into account geographic <span class="snippet">and</span> temporal side information. The framework is applied to Foursquare data <span class="snippet">and</span> used to detect the dominant topics in the neighbourhoods of a city. In particular, we present the most prominent topics discussed by Foursquare users in New York, London, Chicago <span class="snippet">and</span> San Francisco. We characterize the topics' spatial coverage <span class="snippet">and</span> temporal evolution, <span class="snippet">and</span> we also highlight some cultural idiosyncrasies. Finally, we evaluate the novel spatio-temporal topic model quantitatively. We believe that our model may be a useful tool for social scientists <span class="snippet">and</span> application developers.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5928741&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Development of visualizations for <span class="snippet">social network analysis</span> of chatroom text
									</a>
								
							
						
					
				</h3>
				
					
						McIntire, J.; Osesina, O.I.; Craft, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5875054">
			                	<br>Collaboration Technologies <span class="snippet">and</span> Systems (CTS), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967026">
								Aerospace</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294961108">
								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Engineered Materials, Dielectrics &amp; Plasmas</a>
						   
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								Engineering Profession</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967113">
								Photonics &amp; Electro-Optics</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967042">
								Power, Energy, &amp; Industry Applications</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967010">
								Robotics &amp; Control Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967025">
								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CTS.2011.5928741" target="blank">
									10.1109/CTS.2011.5928741
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										In this paper a set of visualization tools <span class="snippet">and</span> related ideas are introduced for extracting <span class="snippet">and</span> analyzing social network features present within persistent, multi-speaker, multi-topic, quasi-synchronous computer-mediated communication systems enacted over the internet, i.e., internet chatrooms. Preliminary models of these tools are applied to a real-world chatroom dataset. Results suggest the utility of such tools for enhancing the usability of digital text communications <span class="snippet">and</span> the understanding of social structures <span class="snippet">and</span> dynamics within the virtual world. Potentially promising visualization methods <span class="snippet">and</span> areas of future research are discussed.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6406284&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Psychological Aspects of Social Communities
									</a>
								
							
						
					
				</h3>
				
					
						Friggeri, A.; Lambiotte, R.; Kosinski, M.; Fleury, E. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6403618">
			                	<br>Privacy, Security, Risk <span class="snippet">and</span> Trust (PASSAT), 2012 International Conference on <span class="snippet">and</span> 2012 International Confernece on Social Computing (SocialCom)
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.104" target="blank">
									10.1109/SocialCom-PASSAT.2012.104
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										<span class="snippet">Social Network Analysis</span> has often focused on the structure of the network without taking into account the characteristics of the individual involved. In this work, we aim at identifying how individual differences in psychological traits affect the community structure of social networks. Instead of choosing to study only either structural or psychological properties of an individul, our aim is to exhibit in which way the psychological attributes of interacting individuals impacts the social network topology. Using psychological data from the my Personality application <span class="snippet">and</span> social data from Facebook, we confront the personality traits of the subjects to metrics obtained after applying the C<sup>3</sup> community detection algorithm to the social neighborhood of the subjects. We observe that introverts tend to have less communities <span class="snippet">and</span> hide into large communities, whereas extroverts tend to act as bridges between more communities, which are on average smaller <span class="snippet">and</span> of varying cohesion.
										
						                	
						                	
						                	
						                			
						             				
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										Modeling of collaboration social network including temporal attributes
									</a>
								
							
						
					
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						Tutoky, G.; Paralic, J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5937321">
			                	<br>Intelligent Engineering Systems (INES), 2011 15th IEEE International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								<a href="http://dx.doi.org/10.1109/INES.2011.5954772" target="blank">
									10.1109/INES.2011.5954772
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						 <br>Publication Year: 2011
							
										
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										In this paper we analyze basic approaches for modeling of collaboration networks <span class="snippet">and</span> propose some new extensions in weighting ties among event participants. One of the extensions is based on the idea of aging of the ties among collaborators. Classical as well as newly proposed approaches to weighting of ties in collaboration networks are experimentally evaluated. We performed two experiments with real data set <span class="snippet">and</span> as a reference we used data gathered from peoples' opinions expressed in targeted inquiries.
										
						                	
						                	
						                	
						                			
						             				
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										A Generic Architecture for a Social Network Monitoring <span class="snippet">and</span> Analysis System
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						Semenov, A.; Veijalainen, J.; Boukhanovsky, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6041102">
			                	<br>Network-Based Information Systems (NBiS), 2011 14th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								<a href="http://dx.doi.org/10.1109/NBiS.2011.52" target="blank">
									10.1109/NBiS.2011.52
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						 <br>Publication Year: 2011
							
										
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										This paper describes the architecture <span class="snippet">and</span> a partial implementation of a system designed for the monitoring <span class="snippet">and</span> analysis of communities at social media sites. The main contribution of the paper is a novel system architecture that facilitates long-term monitoring of diverse social networks existing <span class="snippet">and</span> emerging at various social media sites. It consists of three main modules, the crawler, the repository <span class="snippet">and</span> the analyzer. The first module can be adapted to crawl different sites based on ontology describing the structure of the site. The repository stores the crawled <span class="snippet">and</span> analyzed persistent data using efficient data structures. It can be implemented using special purpose graph databases <span class="snippet">and</span>/or object-relational database. The analyzer hosts modules that can be used for various graph <span class="snippet">and</span> multimedia contents analysis tasks. The results can be again stored to the repository, <span class="snippet">and</span> so on. All modules can be run concurrently.
										
						                	
						                	
						                	
						                			
						             				
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										First Steps to Netviz Nirvana: Evaluating <span class="snippet">Social Network Analysis</span> with NodeXL
									</a>
								
							
						
					
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						Bonsignore, E.M.; Dunne, C.; Rotman, D.; Smith, M.; Capone, T.; Hansen, D.L.; Shneiderman, B. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5282954">
			                	<br>Computational Science <span class="snippet">and</span> Engineering, 2009. CSE '09. International Conference on
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		       			<br>  
						Volume:	4					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/CSE.2009.120" target="blank">
									10.1109/CSE.2009.120
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						 <br>Publication Year: 2009
							
										
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										<span class="snippet">Social Network Analysis</span> (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners <span class="snippet">and</span> students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics <span class="snippet">and</span> graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier-to-entry framework for teaching <span class="snippet">and</span> learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds <span class="snippet">and</span> were chosen specifically for their experience with graph drawing <span class="snippet">and</span> information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users <span class="snippet">and</span> (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, <span class="snippet">and</span> significantly, the power of a tightly integrated metrics/visualization tool to spark insight <span class="snippet">and</span> facilitate sense-making for students of SNA.
										
						                	
						                	
						                	
						                			
						             				
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										Classification Analysis in Complex <span class="snippet">Online</span> Social Networks Using Semantic Web Technologies
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						Opuszko, Marek; Ruhland, Johannes 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6423126">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2012 IEEE/ACM International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2012.179" target="blank">
									10.1109/ASONAM.2012.179
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						 <br>Publication Year: 2012
							
										
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										The Semantic Web enables people <span class="snippet">and</span> computers to interact <span class="snippet">and</span> exchange information. Based on Semantic Web technologies, different machine learning applications have been designed. Particularly important is the possibility to create complex metadata descriptions for any problem domain, based on pre-defined ontologies. In this paper we evaluate the use of a semantic similarity measure based on pre-defined ontologies as an input for a classification analysis in the context of <span class="snippet">social network analysis</span>. A link prediction between actors of two real world social networks is performed, which could serve as a recommendation system. The social networks involve different types of relations <span class="snippet">and</span> nodes. We measure the prediction performance based on a semantic similarity measure as well as traditional approaches. The findings demonstrate that the prediction accuracy based on the semantic similarity is comparable to traditional approaches <span class="snippet">and</span> shows that data mining on complex social networks using ontology-based metadata can be considered as a very promising approach.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6028667&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Service delivery using <span class="snippet">social network analysis</span> in P2P supportive e-learning environment
									</a>
								
							
						
					
				</h3>
				
					
						Ye Conghuan 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6018326">
			                	<br>Computer Science &amp; Education (ICCSE), 2011 6th International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICCSE.2011.6028667" target="blank">
									10.1109/ICCSE.2011.6028667
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Along with the education informatization thorough development there is no more difficulty to manage most of learning contents <span class="snippet">and</span> deliver learning service to learners in e-learning system. Nowadays, learners desire to receive his/her own learning services <span class="snippet">and</span> interact with ones who have same learning interest. However, the current e-learning system couldn't solve e-learning resource sharing among learners who have the same learning interest. In this paper, we propose a learning service delivery model in P2P supportive e-learning system with <span class="snippet">social network analysis</span>. The new framework has a growing interest in raise the utilization rate of those learning resources in university, at the same time, learners who have common learning interest could ubiquitously access the learning service delivered <span class="snippet">and</span> interact with each other via P2P communication technology. In contrast to the existing traditional solutions for e-learning service delivery, our approach is interest-centric <span class="snippet">and</span> can be used by the provider to quickly deliver services with decrease of delivery costs.
										
						                	
						                	
						                	
						                			
						             				
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										Semantic image annotation with social context
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						Elahi, N.; Karlsen, R.; Younas, W. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5672505">
			                	<br>Internet Technology <span class="snippet">and</span> Secured Transactions (ICITST), 2010 International Conference for
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										The purpose of our system is to semi-automatically generate ontology-based annotations for a social network by leveraging the annotations provided by the most active user (also known as the central actor). Social context of an image is of central importance in our approach towards semantic semi-automatic annotation. For the context of an image, we consider several factors like geo-reference, time <span class="snippet">and</span> relationship among actors in social networks. Our system leverages the context, which is automatically available along with the image. We have also extended <span class="snippet">Social Network Analysis</span> techniques by considering the granularity of relationships among actors under consideration. In this paper a semantic web framework was used to represent the social network <span class="snippet">and</span> to deal with the diversity of relationships. OntoCAIM ontology is developed which not only encompasses <span class="snippet">Social Network Analysis</span> functionality but also defines mechanism to annotate the images with an underlying ontology.
										
						                	
						                	
						                	
						                			
						             				
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										Constructing a Cloud Computing Based Social Networks Data Warehousing <span class="snippet">and</span> Analyzing System
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						I-Hsien Ting; Chia-Hung Lin; Chen-Shu Wang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.25" target="blank">
									10.1109/ASONAM.2011.25
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						 <br>Publication Year: 2011
							
										
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										The research area of Social networks analysis has been recognized as extremely time-consuming tasks as well as large storage space is always necessary in order to store the social data, especially to deal with the data in the World Wide Web. Therefore, how to design an architecture <span class="snippet">and</span> environment for performing social networks analysis is very essential. In this paper, we proposed a data warehousing <span class="snippet">and</span> analyzing system which is based on the concept of cloud computing. The system has also been implemented <span class="snippet">and</span> evaluated under the proposed environment with different cloud computing approaches.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231939&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Collaborative Intensity in Social Networks
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						Stein, K.; Blaschke, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.18" target="blank">
									10.1109/ASONAM.2009.18
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										Research on collaboration in social networks is largely restricted by a lack of longitudinal data. Approximations of collaborative intensity necessarily rely on the width of collaboration such as the number of papers coauthered by two scientists. In contrast, we discuss precise measures of collaborative intensity with respect to not only the width but also the depth of collaboration. Based on empirical data of four social networks, we compare a widely-used approximation with our own measures of collaborative intensity.We find that the quality of the approximation varies with the type of social networks.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5722549&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										<span class="snippet">Social Network Analysis</span> of trends in the consumer electronics domain
									</a>
								
							
						
					
				</h3>
				
					
						Niazi, M.A.; Hussain, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5715001">
			                	<br>Consumer Electronics (ICCE), 2011 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Engineered Materials, Dielectrics &amp; Plasmas</a>
						   
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								Fields, Waves &amp; Electromagnetics</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICCE.2011.5722549" target="blank">
									10.1109/ICCE.2011.5722549
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										We present a study of the trends in the consumer electronics domain using Complex <span class="snippet">Social Network Analysis</span> (SNA) of citation data retrieved from the Thomson Reuters Web of Knowledge. Our findings include the identification of the most influential papers <span class="snippet">and</span> journals in the domain. In addition, our analysis provides proof of the key role of IEEE Transactions on Consumer Electronics in the domain of “Consumer Electronics” in terms of both Centrality as well as frequency of articles.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5730307&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A quantitative methodology to identify relevant users in social networks
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				</h3>
				
					
						Canali, C.; Casolari, S.; Lancellotti, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5724309">
			                	<br>Business Applications of <span class="snippet">Social Network Analysis</span> (BASNA), 2010 IEEE International Workshop on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/BASNA.2010.5730307" target="blank">
									10.1109/BASNA.2010.5730307
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										Social networks are gaining an increasing popularity on the Internet, with tens of millions of registered users <span class="snippet">and</span> an amount of exchanged contents accounting for a large fraction of the Internet traffic. Due to this popularity, social networks are becoming a critical media for business <span class="snippet">and</span> marketing, as testified by viral advertisement campaigns based on such networks. To exploit the potential of social networks, it is necessary to classify the users in order to identify the most relevant ones. For example, in the context of marketing on social networks, it is necessary to identify which users should be involved in an advertisement campaign. However, the complexity of social networks, where each user is described by a large number of attributes, transforms the problem of identifying relevant users in a needle in a haystack problem. Starting from a set of user attributes that may be redundant or do not provide significant information for our analysis, we need to extract a limited number of meaningful characteristics that can be used to identify relevant users. We propose a quantitative methodology based on Principal Component Analysis (PCA) to analyze attributes <span class="snippet">and</span> extract characteristics of social network users from the initial attribute set. The proposed methodology can be applied to identify relevant users in social network for different types of analysis. As an application, we present two case studies that show how the proposed methodology can be used to identify relevant users for marketing on the popular YouTube network. Specifically, we identify which users may play a key role in the content dissemination <span class="snippet">and</span> how users may be affected by different dissemination strategies.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6149101&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Building Social Decision Support Mechanisms with Friend Networks
									</a>
								
							
						
					
				</h3>
				
					
						Yung-Ming Li; Yi-Lin Lee 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6148328">
			                	<br>System Science (HICSS), 2012 45th Hawaii International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/HICSS.2012.135" target="blank">
									10.1109/HICSS.2012.135
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						 <br>Publication Year: 2012
							
										
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										Many Internet users are getting used to make decisions based on opinions collected from their own social networks. While conventional decision support system has been extensively investigated, little specific mechanism, however, on how social networks can help users with decision-making is developed. Using <span class="snippet">social network analysis</span> along with regression model, fuzzy Delphi <span class="snippet">and</span> fuzzy AHP methods as tools, this paper designs a social network based decision support system with better effectiveness.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231938&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Groupthink <span class="snippet">and</span> Peer Pressure: Social Influence in <span class="snippet">Online</span> Social Network Groups
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				</h3>
				
					
						Pan Hui; Buchegger, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.17" target="blank">
									10.1109/ASONAM.2009.17
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										In this paper, we present a horizontal view of social influence, more specifically a quantitative study of the influence of neighbours on the probability of a particular node to join a group, on four popular <span class="snippet">Online</span> Social Networks (OSNs), namely Orkut, YouTube, LiveJournal, <span class="snippet">and</span> Flickr. Neighbours in OSNs have a mutually acknowledged relation, most often defined as friendship, <span class="snippet">and</span> they are directly connected on a graph of a social network. Users in OSNs can also join groups of users. These groups represent common areas of interest.We present a simple social influence model to describe <span class="snippet">and</span> explain the group joining process of users on OSNs. To this end, we extract the social influence from data sets of OSNs of a million sample nodes. One of our findings is that a set of neighbours in the OSN is about 100 times more powerful in influencing a user to join a group than the same number of strangers.
										
						                	
						                	
						                	
						                			
						             				
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										A Trustable Reputation Scheme Based on Private Relationships
									</a>
								
							
						
					
				</h3>
				
					
						Shin-Yan Chiou; Shih-Ying Chang; Mezzour, G.; Perrig, A.; Hung-Min Sun 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2009.8" target="blank">
									10.1109/ASONAM.2009.8
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										<span class="snippet">Online</span> reviews are widely used for purchase decisions. Their trustworthiness is limited, however, by fake reviews. Fortunately, opinions from friends in a social network are more reliable but less convenient to obtain. Combining the advantages purchase decisions of <span class="snippet">online</span> reviews <span class="snippet">and</span> opinions from friends can be achieved by enabling users to recognize the <span class="snippet">online</span> reviews originating from their friends. By leveraging buyerspsila trust to nearby friends within their social network, it is possible to provide them in some cases with <span class="snippet">online</span> reviews they can entirely trust. In this paper we present techniques to enable users to recognize the <span class="snippet">online</span> reviews from their friends in a privacy-preserving manner. Our approach has many applications such as Internet auctions <span class="snippet">and</span> <span class="snippet">online</span> gaming.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5455248&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Team Development Measurement by Dynamic Fuzzy <span class="snippet">Social Network Analysis</span>
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						Lixin Zhou 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173">
			                	<br>Information Science <span class="snippet">and</span> Engineering (ICISE), 2009 1st International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICISE.2009.1158" target="blank">
									10.1109/ICISE.2009.1158
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						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										How to obtain a high performing team quickly <span class="snippet">and</span> effectively is very important in a project management. Communication is most essential part in a project team. In this paper, a method of measuring team performance by dynamic <span class="snippet">social network analysis</span> is put forward. With dynamic fuzzy <span class="snippet">social network analysis</span>, we can find the organizational structure of a team, the pattern of communication in a team. Then, the performance of a team can be analyzed by the organizational structure <span class="snippet">and</span> communication pattern of a team.
										
						                	
						                	
						                	
						                			
						             				
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										To form a smaller world in the research realm of hierarchical decision models
									</a>
								
							
						
					
				</h3>
				
					
						Bing Wang; Xiaotian Yao 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6103492">
			                	<br>Industrial Engineering <span class="snippet">and</span> Engineering Management (IEEM), 2011 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Photonics &amp; Electro-Optics</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/IEEM.2011.6118223" target="blank">
									10.1109/IEEM.2011.6118223
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						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Hierarchical decision models (HDMs) have been used widely in industrial <span class="snippet">and</span> academic fields. The world of research in HDM is shrinking due to the connectivity of collaborative relationships between researchers. For new research to become widely known <span class="snippet">and</span> for collaboration to be done in HDMs, we want to identify <span class="snippet">and</span> connect the clusters of the researchers <span class="snippet">and</span> the central member of the clusters. We use the <span class="snippet">social network analysis</span> method to analyze the network of HDM researchers connected by coauthorship in the selected papers. We find out the important researchers with highest degree centralities <span class="snippet">and</span> publication frequencies <span class="snippet">and</span> the key researchers among the top 8 components. We act as the “information gatekeeper” by connecting the identified researchers so that the average distance between the vertices is eliminated significantly <span class="snippet">and</span> thus construct a smaller world in the research realm about HDMs.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6261696&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										<span class="snippet">Social network analysis</span> algorithm on a many-core GPU
									</a>
								
							
						
					
				</h3>
				
					
						Sang Won Seo; Joohyun Kyong; Eun-Jin Im 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6252853">
			                	<br>Ubiquitous <span class="snippet">and</span> Future Networks (ICUFN), 2012 Fourth International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICUFN.2012.6261696" target="blank">
									10.1109/ICUFN.2012.6261696
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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									<p>
										Proliferation of social network service permeates lives of people on internet, <span class="snippet">and</span> its social, political <span class="snippet">and</span> cultural significance prompts the need to understand <span class="snippet">and</span> to analyze the contents <span class="snippet">and</span> structures of such services. The sheer volume of such social network is enormous, <span class="snippet">and</span> it necessitates development <span class="snippet">and</span> implementation of efficient <span class="snippet">social network analysis</span> algorithms. Among these, Influence Maximization is one example of such algorithm. The objective of the influence maximization algorithm is to find a small subset of nodes, so-called seed-nodes, that result in maximization of the spread of influence through the edges in the graph which represents connections in social network. As the cost-efficient, high-performance computing power of many-core GPUs is widely utilized in nearly all areas of computing, we apply our expertise in GPU parallelization to the influence maximization algorithm. The graph algorithms are known as one of sparse algorithms since its irregular data structure requires indirect accesses to memory, resulting in lowbandwidth memory access. Sparse algorithms are one area where many researchers are focused on its efficient parallelization, because the usage of such algorithms is universal <span class="snippet">and</span> thus, vital to broad application areas, from scientific simulations to social studies. In this paper, we introduce algorithms to compute influence maximization of social network, <span class="snippet">and</span> adopt this algorithm to fit parallel implementation on many-core GPU. We also analyze our implementations in terms of factors affecting GPU performance.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6378022&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Soft identification of experts in DBLP using FCA <span class="snippet">and</span> fuzzy rules
									</a>
								
							
						
					
				</h3>
				
					
						Kudelka, M.; Radvansky, M.; Horak, Z.; Kromer, P.; Snasel, V. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6362338">
			                	<br>Systems, Man, <span class="snippet">and</span> Cybernetics (SMC), 2012 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294961108">
								Bioengineering</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Fields, Waves &amp; Electromagnetics</a>
						   
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								Robotics &amp; Control Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICSMC.2012.6378022" target="blank">
									10.1109/ICSMC.2012.6378022
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							1942	- 1947
													
													
						
						
						 
						
					
					
					
			
						
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									<p>
										This study introduces a new soft computing method for expert identification in social networks based on formal concept analysis <span class="snippet">and</span> fuzzy rules. Expert identification is an important task in <span class="snippet">social network analysis</span> <span class="snippet">and</span> there are several methods to identify people who have experience in given area. In this paper, we propose a hybrid approach where the formal concept analysis is used for finding author's profiles based on keywords <span class="snippet">and</span> fuzzy rules to learn the properties of the authors <span class="snippet">and</span> to enhance the set of experts.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6269404&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Based on social network crime organization relation mining <span class="snippet">and</span> central figure determining
									</a>
								
							
						
					
				</h3>
				
					
						Liu Yang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6260028">
			                	<br>Software Engineering <span class="snippet">and</span> Service Science (ICSESS), 2012 IEEE 3rd International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICSESS.2012.6269404" target="blank">
									10.1109/ICSESS.2012.6269404
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										This paper adopts <span class="snippet">Social Network Analysis</span> (SNA) [1] method to find the solution to crime networks mining <span class="snippet">and</span> central figure determining. With the help of Fisher Discriminant Analysis method <span class="snippet">and</span> Criminal Network Core-figure Discovery (CNCD), a threshold discriminating non-conspirators from conspirators <span class="snippet">and</span> the leader of the crime network can be obtained. The paper starts with a small network model, the result may even be used for other fields' network mining <span class="snippet">and</span> core determination, which has important theoretical significance <span class="snippet">and</span> application value.
										
						                	
						                	
						                	
						                			
						             				
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										A degree centrality in multi-layered social network
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						Brodka, P.; Skibicki, K.; Kazienko, P.; Musial, K. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6082090">
			                	<br>Computational Aspects of Social Networks (CASoN), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CASON.2011.6085951" target="blank">
									10.1109/CASON.2011.6085951
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						 <br>Publication Year: 2011
							
										
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										Multi-layered social networks reflect complex relationships existing in modern interconnected IT systems. In such a network each pair of nodes may be linked by many edges that correspond to different communication or collaboration user activities. Multi-layered degree centrality for multi-layered social networks is presented in the paper. Experimental studies were carried out on data collected from the real Web 2.0 site. The multi-layered social network extracted from this data consists of ten distinct layers <span class="snippet">and</span> the network analysis was performed for different degree centralities measures.
										
						                	
						                	
						                	
						                			
						             				
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										Correlation Mining <span class="snippet">and</span> Discovery for Learning Resources
									</a>
								
							
						
					
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						Weng, M.M.; Kau, B.C.; Yen, N.Y. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6267009">
			                	<br>Advanced Learning Technologies (ICALT), 2012 IEEE 12th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ICALT.2012.116" target="blank">
									10.1109/ICALT.2012.116
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										Sharing information <span class="snippet">and</span> resources on the Internet has become an important activity for education. The use of ubiquitous devices makes possible for learning participants to be engaged in an open <span class="snippet">and</span> connected social environment, <span class="snippet">and</span> also allows the learning activities to be performed at any time <span class="snippet">and</span> any places. In this study, the discovery of correlation among shared resources is concentrated. A hypothetical scenario is considered that the information, such as photos <span class="snippet">and</span> thoughts, is applicable to be shared with implicit context (i.e. geographical information) by learners through a practical implementation, PadSCORM, on a mobile device. Two major contributions are achieved. First, the correlations among resources are determined through usage experiences mining <span class="snippet">and</span> geographical information adjustment. It then assists learners in filtering out redundant information by highlighting the significance of resources. Second, an intelligent searching algorithm is proposed to visualize adaptive routes in order to facilitate search process <span class="snippet">and</span> to enrich the learning activity.
										
						                	
						                	
						                	
						                			
						             				
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										Agent-based Modelling of Social Organisations
									</a>
								
							
						
					
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						Kozlak, J.; Zygmunt, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5986779">
			                	<br>Complex, Intelligent <span class="snippet">and</span> Software Intensive Systems (CISIS), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CISIS.2011.76" target="blank">
									10.1109/CISIS.2011.76
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										In the paper, the model of the society represented by a social network <span class="snippet">and</span> the model of a multi-agent system built on the basis of this, is presented. The particular aim of the system is to predict the evolution of a society <span class="snippet">and</span> an analysis of the communities that appear, their characteristic features <span class="snippet">and</span> reasons for coming into being. As an example of application, an analysis was made of a social portal which makes it possible to offer <span class="snippet">and</span> reserve places in rooms for travelling tourists.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5562788&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										White's Three Disciplines <span class="snippet">and</span> Relative Valuation Order: Countering the Social Ignorance of Automated Data Collection <span class="snippet">and</span> Analysis
									</a>
								
							
						
					
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						McDermott, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/ASONAM.2010.16" target="blank">
									10.1109/ASONAM.2010.16
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										This paper asks which of White's (2009) three disciplines <span class="snippet">and</span> relative valuation orders does the Singapore blogosphere adhere to. Analysing not just the hyperlink connections but the textual discourse; <span class="snippet">and</span> in doing so attempts to highlight certain limitations of using automated data mining <span class="snippet">and</span> analysis software. Using the Singapore blogosphere, described by Lin, Sundaram, Chi, Tatemura, <span class="snippet">and</span> Tseng, (2006) <span class="snippet">and</span> Hurst (2006), as an isolated <span class="snippet">and</span> distinct network with no theme or focus, I have targeted blogs using <span class="snippet">social network analysis</span> uncovering the key players, with higher levels of `betweenness centrality' (de Nooy &amp; Mrvar et al., 2005) <span class="snippet">and</span> the themes <span class="snippet">and</span> discipline of the Singapore blogosphere. This case study will help highlight the analytic framework, benefits <span class="snippet">and</span> limitations of using <span class="snippet">social network analysis</span> <span class="snippet">and</span> an ethnographical approach to networks. This paper also highlights the use of various software technology; blogs, IssueCrawler, HTTrack, NetDraw, <span class="snippet">and</span> Leximancer while using an ethnographic approach to counter the social ignorance of automated electronic software.
										
						                	
						                	
						                	
						                			
						             				
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										Collaborative Mining in Multiple Social Networks Data for Criminal Group Discovery
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				</h3>
				
					
						Fard, A.M.; Ester, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5282954">
			                	<br>Computational Science <span class="snippet">and</span> Engineering, 2009. CSE '09. International Conference on
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		       			<br>  
						Volume:	4					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/CSE.2009.435" target="blank">
									10.1109/CSE.2009.435
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						 <br>Publication Year: 2009
							
										
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										The hidden knowledge in social networks data can be regarded as an important resource for criminal investigations which can help finding the structure <span class="snippet">and</span> organization of a criminal network. However such network based analysis has not been studied in an applied way <span class="snippet">and</span> remains mostly a manual process. To assist inspectors <span class="snippet">and</span> intelligence agencies discover this knowledge, we defined a new problem <span class="snippet">and</span> then proposed a framework for automated network data analysis <span class="snippet">and</span> deduction approach from multiple social networks by converting to transaction dataset, applying association mining, <span class="snippet">and</span> statistical methods. By applying a game theory concept in a multi-agent model, we try to design a policy for knowledge discovery <span class="snippet">and</span> inference fusion. This approach enables police stations to build <span class="snippet">and</span> deploy P2P applications through a unified medium for finding criminals relationship <span class="snippet">and</span> identifying suspicious guys.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5960121&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Evaluating knowledge flow in multirelational scientific social networks
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						Stroele, V.; Zimbrao, G.; Souza, J.M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5941185">
			                	<br>Computer Supported Cooperative Work in Design (CSCWD), 2011 15th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CSCWD.2011.5960121" target="blank">
									10.1109/CSCWD.2011.5960121
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										Social networks are dynamic social structures consisting of individuals or organizations, usually represented by nodes tied by one or more types of relationships. Analyzing these structures allows us to detect several inter <span class="snippet">and</span> intra connections between people, inside <span class="snippet">and</span> outside their organizations. In this context, we construct a multi-relational scientific social network where researchers may have four different types of relationships with each other. Using clustering techniques with max flow measure, we identify the social structure <span class="snippet">and</span> research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific scenario of the Computing Sciences.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6113125&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Get <span class="snippet">Online</span> Support, Feel Better -- Sentiment Analysis <span class="snippet">and</span> Dynamics in an <span class="snippet">Online</span> Cancer Survivor Community
									</a>
								
							
						
					
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						Baojun Qiu; Kang Zhao; Mitra, P.; Dinghao Wu; Caragea, C.; Yen, J.; Greer, G.E.; Portier, K. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6112285">
			                	<br>Privacy, security, risk <span class="snippet">and</span> trust (passat), 2011 ieee third international conference on <span class="snippet">and</span> 2011 ieee third international conference on social computing (socialcom)
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
					
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/PASSAT/SocialCom.2011.127" target="blank">
									10.1109/PASSAT/SocialCom.2011.127
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Many users join <span class="snippet">online</span> health communities (OHC) to obtain information <span class="snippet">and</span> seek social support. Understanding the emotional impacts of participation on patients <span class="snippet">and</span> their informal caregivers is important for OHC managers. Ethnographical observations, interviews, <span class="snippet">and</span> questionnaires have reported benefits from <span class="snippet">online</span> health communities, but these approaches are too costly to adopt for large-scale analyses of emotional impacts. A computational approach using machine learning <span class="snippet">and</span> text mining techniques is demonstrated using data from the American Cancer Society Cancer Survivors Network (CSN), an <span class="snippet">online</span> forum of nearly a half million posts. This approach automatically estimates the sentiment of forum posts, discovers sentiment change patterns in CSN members, <span class="snippet">and</span> allows investigation of factors that affect the sentiment change. This first study of sentiment benefits <span class="snippet">and</span> dynamics in a large-scale health-related electronic community finds that an estimated 75%-85% of CSN forum participants change their sentiment in a positive direction through <span class="snippet">online</span> interactions with other community members. Two new features, Name <span class="snippet">and</span> Slang, not previously used in sentiment analysis, facilitate identifying positive sentiment in posts. This work establishes foundational concepts for further studies of sentiment impact of OHC participation <span class="snippet">and</span> provides insight useful for the design of new OHC's or enhancement of existing OHCs in providing better emotional support to their members.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5992644&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Social Brokerage behind Knowledge Sharing
									</a>
								
							
						
					
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						Fang-Ling Lin; Guey-Fa Chiou 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.35" target="blank">
									10.1109/ASONAM.2011.35
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										This study explored the implicit brokerage positions of social network behind knowledge sharing <span class="snippet">and</span> demonstrated applicability to researches of community of practices. A concept of knowledge brokerage was proposed on the basis of the theory of social brokerage, <span class="snippet">and</span> hypotheses were developed to verify the relational properties of brokerage opportunities. <span class="snippet">Social network analysis</span> (SNA) was applied to conduct the egocentric network surveys <span class="snippet">and</span> related analyses. Several new ways of evaluating the performance of knowledge sharing have been developed. The results verified the usability of brokerage as indicators of knowledge sharing. The results also can reveal efficiencies <span class="snippet">and</span> deficiencies in knowledge dissemination among members of communities. These findings hold relevance for studies on the information technology practices of other professionals.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5730299&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Community discovery in a growing model of social networks
									</a>
								
							
						
					
				</h3>
				
					
						Bhukya, S. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5724309">
			                	<br>Business Applications of <span class="snippet">Social Network Analysis</span> (BASNA), 2010 IEEE International Workshop on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/BASNA.2010.5730299" target="blank">
									10.1109/BASNA.2010.5730299
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										A number of recent studies on social networks are based on characteristics which include assortative mixing, high clustering, short average path lengths, broad degree distributions <span class="snippet">and</span> the existence of community structure. Here, a model which satisfies all the above characteristics is developed. In addition, this model facilitates interaction between different communities. This model gives very high clustering coefficient by retaining the asymptotically scale-free degree distribution. Here the community structure is raised from a mixture of random attachment <span class="snippet">and</span> implicit preferential attachment. A model for community discovery also has been discovered where strict community structure has been preserved.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5380625&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Bowling Alone <span class="snippet">and</span> Trust Decline in Social Network Sites
									</a>
								
							
						
					
				</h3>
				
					
						Massa, P.; Salvetti, M.; Tomasoni, D. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379503">
			                	<br>Dependable, Autonomic <span class="snippet">and</span> Secure Computing, 2009. DASC '09. Eighth IEEE International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/DASC.2009.130" target="blank">
									10.1109/DASC.2009.130
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										In this paper we analyze the community of a social network site, Advogato. The peculiar characteristics of Advogato is that users can explicitly express weighted trust relationships among themselves. We conduct a longitudinal analysis of the trust network over a time period of 4 years, exploring the community as it grew from a knit circle of 300 users to an society of almost 6500 individuals. We report the changes over time of standard indexes in <span class="snippet">social network analysis</span> such as clustering <span class="snippet">and</span> degrees of separation. We then focus on specific measures about trust such as reciprocity <span class="snippet">and</span> changes over time of average trust. A decline in trust is observed as the community grows. Following what we believe to be the first empirical analysis of trust evolution over time in a real community, we conclude suggesting how the availability of data about human relationships in social network sites is opening up the possibility of monitoring changes in trust in real time. In order to foster this research line, we released the datasets <span class="snippet">and</span> the code we used in our analysis.
										
						                	
						                	
						                	
						                			
						             				
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										Learning About the Quality of Teamwork from Wikiteams
									</a>
								
							
						
					
				</h3>
				
					
						Turek, P.; Wierzbicki, A.; Nielek, R.; Hupa, A.; Datta, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5590331">
			                	<br>Social Computing (SocialCom), 2010 IEEE Second International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SocialCom.2010.13" target="blank">
									10.1109/SocialCom.2010.13
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										This paper describes an approach to evaluating teams of contributors in Wikipedia based on <span class="snippet">social network analysis</span>. We present the idea of creating an implicit social network based on characteristics of pages' edit history <span class="snippet">and</span> collaboration between contributors. This network consists of four dimensions: trust, distrust, acquaintance <span class="snippet">and</span> knowledge. Trust <span class="snippet">and</span> distrust are based on content modifications (copying <span class="snippet">and</span> deleting respectively), acquaintance is based on the amount of discussion on articles' talk pages between a given pair of authors <span class="snippet">and</span> knowledge is based on the categories in which an author typically contributes. Our social network is based on the entire Wikipedia edit history, <span class="snippet">and</span> therefore is a summary of all recorded author interactions. This social network can be used to assess the quality of a team of authors <span class="snippet">and</span> consequently, to recommend good teams. The social network can also be used by Wikipedia authors <span class="snippet">and</span> editors as an additional tool that allows to improve the author's collaboration, as it expresses each author's social environment <span class="snippet">and</span> can be navigated to discover new projects that an author can participate in, or to recommend new collaborators.
										
						                	
						                	
						                	
						                			
						             				
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										A cooperative social <span class="snippet">and</span> vehicular network <span class="snippet">and</span> its dynamic bandwidth allocation algorithms
									</a>
								
							
						
					
				</h3>
				
					
						Ridong Fei; Kun Yang; Xueqi Cheng 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5888675">
			                	<br>Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Engineering Profession</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/INFCOMW.2011.5928891" target="blank">
									10.1109/INFCOMW.2011.5928891
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										This paper investigates into a special type of network that encourages cooperation between its upper layer social network <span class="snippet">and</span> lower layer wireless communication network with particular focus on commuters on the road. Regarded as a branch of mobile social networks, the so-called vehicular social network (VSNs) proposed in this paper needs to consider both social aspects (such as social ties through common interests) <span class="snippet">and</span> physical network operational mechanisms to design a system that is effective to its users <span class="snippet">and</span> efficient to network resources. This calls for in-depth investigations into the correlation between social behaviours <span class="snippet">and</span> network algorithms. This paper aims to making some preliminary exploration into this exciting field by investigating into how social centrality, an important concept in <span class="snippet">social network analysis</span>, makes impact on dynamic bandwidth allocation (DBA) in a specific scenario of vehicular social networks. In particular the paper introduces centrality into the utility function's formula, based on which DBA is carried out in IEEE 802.16j-enable vehicular networks.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5178967&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A brief survey of computational approaches in Social Computing
									</a>
								
							
						
					
				</h3>
				
					
						King, I.; Jiexing Li; Kam Tong Chan 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5161636">
			                	<br>Neural Networks, 2009. IJCNN 2009. International Joint Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/IJCNN.2009.5178967" target="blank">
									10.1109/IJCNN.2009.5178967
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						 <br>Publication Year: 2009
							
										
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									<p>
										Web 2.0 technologies have brought new ways of connecting people in social networks for collaboration in various on-line communities. Social Computing is a novel <span class="snippet">and</span> emerging computing paradigm that involves a multi-disciplinary approach in analyzing <span class="snippet">and</span> modeling social behaviors on different media <span class="snippet">and</span> platforms to produce intelligent <span class="snippet">and</span> interactive applications <span class="snippet">and</span> results. In this paper, we give a brief survey of the various machine learning <span class="snippet">and</span> computational techniques used in Social Computing by first examining the social platforms, e.g., social network sites, social media, social games, social bookmarking, <span class="snippet">and</span> social knowledge sites, where computational methodology is required to collect, extract, process, mine, <span class="snippet">and</span> visualize the data. We then present surveys on more specific instances of computation tasks <span class="snippet">and</span> techniques, e.g., <span class="snippet">social network analysis</span>, link modeling <span class="snippet">and</span> mining, ranking, sentiment analysis, etc., that are being used on these social platforms to obtain desirable results. Lastly, we present a small subset of an extensive reference list, which contains over 140 highly relevant references relating to the recent development in the computational aspects of Social Computing.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5563065&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Global Measure for Estimating the Degree of Organization of Terrorist Networks
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				</h3>
				
					
						Dawoud, K.; Alhajj, R.; Rokne, J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.84" target="blank">
									10.1109/ASONAM.2010.84
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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							421	- 427
													
													
						
						
						 
						
					
					
					
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										The motivation for the study described in this paper is realizing the fact that organizational structure of a group is a key indicator in determining its strengths <span class="snippet">and</span> weaknesses. A general knowledge of the prevalent models of terrorist organizations leads to a better understanding of their capabilities. Knowledge of the different labels <span class="snippet">and</span> systems of classification that have been applied to groups <span class="snippet">and</span> individuals aid us in discarding useless or irrelevant terms, <span class="snippet">and</span> in understanding the purposes <span class="snippet">and</span> usefulness of different terminologies. Previous studies in network analysis have mostly dealt with legal networks with transparent structures. Terrorist networks share some features with conventional (real world) networks, but they are harder to identify because they mostly hide their illicit activities. In this paper we describe a novel approach for extracting structural patterns of terrorist networks with the help of <span class="snippet">social network analysis</span> measures <span class="snippet">and</span> techniques. We propose a global measure for estimating the degree of organization of social networks; the measure is global in terms of being applied to the whole network as an entity <span class="snippet">and</span> being extracted from the major well-known SNA measures. The importance of such research comes from the fact that individuals in organized intellectual networks <span class="snippet">and</span> especially terrorist networks tend to hide their individual rules <span class="snippet">and</span> thus there is a need to deal with such networks as a whole, discovering the degree of organization <span class="snippet">and</span> thus its strengths <span class="snippet">and</span> weaknesses.
										
						                	
						                	
						                	
						                			
						             				
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										Understanding Graph Sampling Algorithms for <span class="snippet">Social Network Analysis</span>
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						Tianyi Wang; Yang Chen; Zengbin Zhang; Tianyin Xu; Long Jin; Pan Hui; Beixing Deng; Xing Li 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5960724">
			                	<br>Distributed Computing Systems Workshops (ICDCSW), 2011 31st International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICDCSW.2011.34" target="blank">
									10.1109/ICDCSW.2011.34
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						 <br>Publication Year: 2011
							
										
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										Being able to keep the graph scale small while capturing the properties of the original social graph, graph sampling provides an efficient, yet inexpensive solution for <span class="snippet">social network analysis</span>. The challenge is how to create a small, but representative sample out of the massive social graph with millions or even billions of nodes. Several sampling algorithms have been proposed in previous studies, but there lacks fair evaluation <span class="snippet">and</span> comparison among them. In this paper, we analyze the state-of art graph sampling algorithms <span class="snippet">and</span> evaluate their performance on some widely recognized graph properties on directed graphs using large-scale social network datasets. We evaluate not only the commonly used node degree distribution, but also clustering coefficient, which quantifies how well connected are the neighbors of a node in a graph. Through the comparison we have found that none of the algorithms is able to obtain satisfied sampling results in both of these properties, <span class="snippet">and</span> the performance of each algorithm differs much in different kinds of datasets.
										
						                	
						                	
						                	
						                			
						             				
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										Analysis of <span class="snippet">online</span> messages for identity tracing in cybercrime investigation
									</a>
								
							
						
					
				</h3>
				
					
						Nirkhi, S.M.; Dharaskar, R.V.; Thakre, V.M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6238218">
			                	<br>Cyber Security, Cyber Warfare <span class="snippet">and</span> Digital Forensic (CyberSec), 2012 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/CyberSec.2012.6246131" target="blank">
									10.1109/CyberSec.2012.6246131
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						 <br>Publication Year: 2012
							
										
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									<p>
										Internet has provided us a much more convenient way to share information all over the world. Cyberspace also opened a new platform for criminal activities. With increased access to computers across the world, cybercrime is becoming a major challenge to law enforcement agencies. Cybercrime investigation process is in its infancy <span class="snippet">and</span> there has been limited success in prosecuting the offenders; therefore a need to understand <span class="snippet">and</span> strengthen the existing investigation methods <span class="snippet">and</span> system for controlling cybercrime is greatly needed. Cybercrime is defined as Illegal computer-mediated activities that can be conducted through global electronic networks. One of the Problem in cybercrime investigation is identity tracing. It is difficult to trace identities due to the anonymity of cybercrime. Therefore tracing the identity of anonymous user has been identified as significant problems which hamper investigations. There is an increase in the number of cybercrime incidents through anonymous emails. Nowadays, government, industries <span class="snippet">and</span> individuals rely on email for communication. Criminals make use of the anonymity in the cyber world to conduct illegitimate activities. Mostly Such activities can be conducted via E-mail. Extracting knowledge <span class="snippet">and</span> information from e-mail text &amp; its analysis provides evidence for cybercrime Investigation. In this paper focus is on analysis of email messages using statistical analysis, <span class="snippet">social network analysis</span> along with their implementation, <span class="snippet">and</span> our proposed method to identify the most plausible author of email texts.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5992585&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Visualizing Bibliographic Databases as Graphs <span class="snippet">and</span> Mining Potential Research Synergies
									</a>
								
							
						
					
				</h3>
				
					
						Varlamis, I.; Tsatsaronis, G. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.52" target="blank">
									10.1109/ASONAM.2011.52
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Bibliographic databases are a prosperous field for data mining research <span class="snippet">and</span> <span class="snippet">social network analysis</span>. They contain rich information, which can be analyzed across different dimensions(e.g., author, year, venue, topic) <span class="snippet">and</span> can be exploited in multiple ways. The representation <span class="snippet">and</span> visualization of bibliographic databases as graphs <span class="snippet">and</span> the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers <span class="snippet">and</span> venues, or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship <span class="snippet">and</span> content similarity information, <span class="snippet">and</span> allows for the formation of scientific networks. Using a graph visualization tool from the biological domain, we are able to provide comprehensive visualizations that help us uncover hidden relations between authors <span class="snippet">and</span> suggest potential synergies between researchers or groups.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5936629&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Emulation of social network ecosystems: Dynamic modeling <span class="snippet">and</span> visualisation on the example of the Indian Agropedia project
									</a>
								
							
						
					
				</h3>
				
					
						Kurz, T.; Rucker, C.; Heistracher, T.J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5902073">
			                	<br>Digital Ecosystems <span class="snippet">and</span> Technologies Conference (DEST), 2011 Proceedings of the 5th IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967026">
								Aerospace</a>
						   
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								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Engineering Profession</a>
						   
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								Fields, Waves &amp; Electromagnetics</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Geoscience</a>
						   
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								Nuclear Engineering</a>
						   
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								Photonics &amp; Electro-Optics</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Robotics &amp; Control Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967025">
								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/DEST.2011.5936629" target="blank">
									10.1109/DEST.2011.5936629
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							Page(s):
					   
						
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										Simulation of social networks that is based on abstract features of existing infrastructures might lack reflectiveness. This paper discusses the multi-agent EvESim framework <span class="snippet">and</span> its application to study processes in the social network Agropedia, which is a digital knowledge repository with an open platform for learning <span class="snippet">and</span> sharing information related to Indian agriculture. The emulation capability of the EvESim framework delivers insights into social network bootstrap <span class="snippet">and</span> growth.
										
						                	
						                	
						                	
						                			
						             				
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										Study on covert networks of terroristic organizations based on text analysis
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						Sun Duo-yong; Guo Shu-Quan; Zhang Hai; Li Ben-xian 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5975309">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics (ISI), 2011 IEEE International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294961108">
								Bioengineering</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ISI.2011.5984117" target="blank">
									10.1109/ISI.2011.5984117
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						 <br>Publication Year: 2011
							
										
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										Text information on typical domestic terrorist organization released by domestic mainstream network media is collected, <span class="snippet">and</span> network text analysis tools are adopted to extract the entities that reflect the social network structure of terroristic organizations <span class="snippet">and</span> the associated data from text information, to construct the covert networks for social organization structure of terrorists. Network analysis tools are adopted to carry out a social network quantitative analysis to the terroristic network characteristics in both individual level <span class="snippet">and</span> organizational level. The study shows, that text information on terroristic activities from various network media can be effectively processed by text data mining, <span class="snippet">and</span> visual network models that reflect social organization structure of terrorists can be made accordingly, <span class="snippet">and</span> this demonstrates that to combine data mining tools with social network analytical method is an effective approach to study the covert networks of terroristic organizations.
										
						                	
						                	
						                	
						                			
						             				
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										Mixed-Membership Stochastic Block-Models for Transactional Networks
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						Shafiei, M.; Chipman, H. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5690658">
			                	<br>Data Mining (ICDM), 2010 IEEE 10th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ICDM.2010.88" target="blank">
									10.1109/ICDM.2010.88
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						 <br>Publication Year: 2010
							
										
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										Transactional network data can be thought of as a list of one-to-many communications (e.g., email) between nodes in a social network. Most social network models convert this type of data into binary relations between pairs of nodes. We develop a latent mixed membership model capable of modeling richer forms of transactional network data, including relations between more than two nodes. The model can cluster nodes <span class="snippet">and</span> predict transactions. The block-model nature of the model implies that groups can be characterized in very general ways. This flexible notion of group structure enables discovery of rich structure in transactional networks. Estimation <span class="snippet">and</span> inference are accomplished via a variational EM algorithm. Simulations indicate that the learning algorithm can recover the correct generative model. Interesting structure is discovered in the Enron email dataset <span class="snippet">and</span> another dataset extracted from the Reddit website. Analysis of the Reddit data is facilitated by a novel performance measure for comparing two soft clusterings. The new model is superior at discovering mixed membership in groups <span class="snippet">and</span> in predicting transactions.
										
						                	
						                	
						                	
						                			
						             				
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										Research of Network Consensus Guidance System Using Community Detection
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				</h3>
				
					
						Huang Yan Hui; Jiang Zhen Gang; Han Rui kai; Chen Bing 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6005185">
			                	<br>Internet Technology <span class="snippet">and</span> Applications (iTAP), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ITAP.2011.6006210" target="blank">
									10.1109/ITAP.2011.6006210
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										Community structure exists in Internet. Research of community detection contributes to a better understanding of network structure. With an eye for network consensus leader analysis, this paper design <span class="snippet">and</span> implement a program to mine network consensus leader based on <span class="snippet">social network analysis</span> method. This paper also design <span class="snippet">and</span> implement GN algorithm using Java language <span class="snippet">and</span> the mining algorithm will be designed for GN algorithm for comparison. The results show that this algorithm improves the community structure of accuracy <span class="snippet">and</span> reduces the time complexity of the algorithm.
										
						                	
						                	
						                	
						                			
						             				
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										Predicting missing links via local feature of common neighbors
									</a>
								
							
						
					
				</h3>
				
					
						Yuxiao Dong; Qing Ke; Jun Rao; Bin Wu 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6012258">
			                	<br>Fuzzy Systems <span class="snippet">and</span> Knowledge Discovery (FSKD), 2011 Eighth International Conference on
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		       			<br>  
						Volume:	2					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/FSKD.2011.6019642" target="blank">
									10.1109/FSKD.2011.6019642
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Predicting missing links in complex networks has a theoretical interest <span class="snippet">and</span> practical significance in <span class="snippet">social network analysis</span>. In this paper, we study the link prediction results as the change of the exponent on common neighbor's degree <span class="snippet">and</span> find some regular pattern between different networks <span class="snippet">and</span> different exponent. Then we present a local network metric based on the regular pattern to estimate the likelihood of the existence of a link between two nodes. Our new metric takes the exponent of common neighbor's degree into consideration for link prediction. The paper also recommends the value range of the exponent. We compare nine well-known local information metrics <span class="snippet">and</span> the new metric on eight real networks. The result evaluated by AUC indicates that our new metric, namely Degree Exponent Change metric, have a better prediction accuracy than other well-known metrics.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4960372&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Cheat-proof cooperation strategies for wireless live streaming social networks
									</a>
								
							
						
					
				</h3>
				
					
						Lin, W.S.; Zhao, H.V.; Liu, K.J.R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4912736">
			                	<br>Acoustics, Speech <span class="snippet">and</span> Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICASSP.2009.4960372" target="blank">
									10.1109/ICASSP.2009.4960372
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							3469	- 3472
													
													
						
						
						 
						
					
					
					
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Multimedia <span class="snippet">social network analysis</span> is an emerging research area, which analyzes the behavior of users who share multimedia content <span class="snippet">and</span> investigates the impact of human dynamics on multimedia systems. Users watching live streaming in the same wireless network share the same backbone connection to the Internet, thus they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network <span class="snippet">and</span> every user wishes to watch video with as high as possible quality while paying as less as cost for cooperation. Thus full cooperation cannot be guaranteed <span class="snippet">and</span> the cooperation strategy must give incentives to the users. This paper proposes a game-theoretic framework to model user behavior <span class="snippet">and</span> designs incentive-based strategies to stimulate user cooperation in wireless live streaming. We analyze the Pareto optimality <span class="snippet">and</span> time-restricted bargaining equilibrium of the game. We also take into consideration selfish users' cheating behavior <span class="snippet">and</span> propose cheat-proof strategies. Both our analytical <span class="snippet">and</span> simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free <span class="snippet">and</span> help provide reliable services.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
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										Two steps for self-organized social network pre-construction
									</a>
								
							
						
					
				</h3>
				
					
						Wei-Lun Chang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6070513">
			                	<br>Systems, Man, <span class="snippet">and</span> Cybernetics (SMC), 2011 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967046">
								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICSMC.2011.6083779" target="blank">
									10.1109/ICSMC.2011.6083779
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										A social network is a relational connection between individuals, including any relationships related to the exchange of information such as those among friends or colleagues. The most significant problem associated with social networks is establishing the role of key individuals, who are charged with conveying important messages among everyone involved. However, the most appropriate representatives are difficult to identify. The aim of this study was to develop a method with which to facilitate the automatic pre-construction of a social network prior to any interaction <span class="snippet">and</span> pre-identify representatives within the network. The goals of this research were: (1) construct a social network based on a self-organization maps <span class="snippet">and</span> <span class="snippet">social network analysis</span>, (2) verify differences between pre-constructed <span class="snippet">and</span> actual social networks, <span class="snippet">and</span> (3) identify key representatives <span class="snippet">and</span> validate the efficiency of the social network.
										
						                	
						                	
						                	
						                			
						             				
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										Using <span class="snippet">social network analysis</span> technique for supporting software product line process
									</a>
								
							
						
					
				</h3>
				
					
						Jirapanthong, W. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6261593">
			                	<br>Computer Science <span class="snippet">and</span> Automation Engineering (CSAE), 2012 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	3					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967026">
								Aerospace</a>
						   
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Fields, Waves &amp; Electromagnetics</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Geoscience</a>
						   
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								Nuclear Engineering</a>
						   
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								Photonics &amp; Electro-Optics</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Robotics &amp; Control Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
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								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CSAE.2012.6272969" target="blank">
									10.1109/CSAE.2012.6272969
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Software Product Line (SPL) becomes a potential approach for software system engineering. Software artifacts based on different models <span class="snippet">and</span> approaches are collected <span class="snippet">and</span> deposited into repository in order to be reused for a new software product in the future. Otherwise, identifying of reusable software artifacts is still difficult <span class="snippet">and</span> time-consuming. In particular, requirements management for SPL becomes more complex process that should take into account requirements value for customers, cost of implementation, available resources, requirements interdependencies, system architecture <span class="snippet">and</span> dependencies to the code base. In this paper we propose a social network representing the software product line artifacts <span class="snippet">and</span> relationships between those artifacts. We believe the social network can be analyzed in order to acquire the information representing the dependencies between software artifacts <span class="snippet">and</span> stakeholders. This information can be applied in order to improve software requirements management for SPL.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5992579&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										The ML-Model for Multi-layer Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Magnani, M.; Rossi, L. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.114" target="blank">
									10.1109/ASONAM.2011.114
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										In this paper we introduce a new model to represent an interconnected network of networks. This model is fundamental to reason about the real organization of on-line social networks, where users belong to <span class="snippet">and</span> interact on different networks at the same time. In addition we extend traditional SNA measures to deal with this multiplicity of networks <span class="snippet">and</span> we apply the model to a real dataset extracted from two microblogging sites.
										
						                	
						                	
						                	
						                			
						             				
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										<span class="snippet">Social network analysis</span> in epidemiology: Current trends <span class="snippet">and</span> perspectives
									</a>
								
							
						
					
				</h3>
				
					
						Stattner, E.; Vidot, N. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992815">
			                	<br>Research Challenges in Information Science (RCIS), 2011 Fifth International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/RCIS.2011.6006866" target="blank">
									10.1109/RCIS.2011.6006866
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										The increasing development of world trade has promoted the spread of diseases. Recent events, like the H<sub>1</sub>N<sub>1</sub> outbreak <span class="snippet">and</span> spread, have confirmed this tendency. Everyone could observe that this kind of infectious disease often has the ability to cross countries borders <span class="snippet">and</span> spread rapidly. Preventing, monitoring <span class="snippet">and</span> controlling the outbreak of these diseases have appeared to be a public health problem of primary importance for years. However, although numerous works have been conducted in order to understand <span class="snippet">and</span> study epidemics, this subject remains a topical issue because of the complexity of the phenomena. In this paper, we present new opportunities offered by the field of social networks for understanding the spread of infectious diseases. This work focuses both on techniques already used in epidemiology, <span class="snippet">and</span> very recent works in <span class="snippet">social network analysis</span> that open new perspectives. We give an overview of methods currently used to model <span class="snippet">and</span> study the spread of diseases <span class="snippet">and</span> future directions that the social network approach allows to glimpse.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5176114&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Discovering Communities of Interest in a Tagged On-Line Environment
									</a>
								
							
						
					
				</h3>
				
					
						Kammergruber, W.C.; Viermetz, M.; Ziegler, C.-N. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5176085">
			                	<br>Computational Aspects of Social Networks, 2009. CASON '09. International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/CASoN.2009.22" target="blank">
									10.1109/CASoN.2009.22
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									 Cited by 2
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Tagging <span class="snippet">and</span> social networks have come into increasing use in concert with the rise of collaborative <span class="snippet">and</span> interactive on-line media. The focus of tagging is herein twofold: First of all the plain annotation of existing data by a governing instance in order to increase the semantic content of unstructured data, <span class="snippet">and</span> secondly the application of such meta-information by a community or a group of like minded users. The information contained in such social tagging reflects the point of view <span class="snippet">and</span> understanding of the community, presenting a valuable source of information for the discovery of community structure,content <span class="snippet">and</span> intent. This paper proposes an approach aimed at the use of community based tagging to address problems in link prediction <span class="snippet">and</span> the discovery of complex user groups in a fleeting <span class="snippet">and</span> unstructured Web-based environment. The ideas presented in this paper are applied to a real world scenario, <span class="snippet">and</span> the results show a distinct opportunity in community detection <span class="snippet">and</span> support. This result will be incorporated into emerging knowledge management systems within Siemens AG in the near future.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6062169&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										StakeSource: harnessing the power of crowdsourcing <span class="snippet">and</span> social networks in stakeholder analysis
									</a>
								
							
						
					
				</h3>
				
					
						Soo Ling Lim; Quercia, D.; Finkelstein, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6062053">
			                	<br>Software Engineering, 2010 ACM/IEEE 32nd International Conference on
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		       			<br>  
						Volume:	2					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1145/1810295.1810340" target="blank">
									10.1145/1810295.1810340
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									<p>
										Projects often fail because they overlook stakeholders. Unfortunately, existing stakeholder analysis tools only capture stakeholders' information, relying on experts to manually identify them. StakeSource is a web-based tool that automates stakeholder analysis. It "crowdsources" the stakeholders themselves for recommendations about other stakeholders <span class="snippet">and</span> aggregates their answers using <span class="snippet">social network analysis</span>.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231851&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Content-Based Clustering for Tag Cloud Visualization
									</a>
								
							
						
					
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						Zubiaga, A.; Garcia-Plaza, A.P.; Fresno, V.; Martinez, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.19" target="blank">
									10.1109/ASONAM.2009.19
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							316	- 319
													
													
						
						
						 
						
					
					
					
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									<p>
										Social tagging systems are becoming an interesting way to retrieve web information from previously annotated data. These sites present a tag cloud made up by the most popular tags, where neither tag grouping nor their corresponding content is considered. We present a methodology to obtain <span class="snippet">and</span> visualize a cloud of related tags based on the use of self-organizing maps, <span class="snippet">and</span> where the relations among tags are established taking into account the textual content of tagged documents. Each map unit can be represented by the most relevant terms of the tags it contains, so that it is possible to study <span class="snippet">and</span> analyze the groups as well as to visualize <span class="snippet">and</span> navigate through the relevant terms <span class="snippet">and</span> tags.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5992609&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Group Evolution Discovery in Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Brodka, P.; Saganowski, S.; Kazienko, P. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2011.69" target="blank">
									10.1109/ASONAM.2011.69
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Group extraction <span class="snippet">and</span> their evolution are among the topics which arouse the greatest interest in the domain of <span class="snippet">social network analysis</span>. However, while the grouping methods in social networks are developed very dynamically, the methods of group evolution discovery <span class="snippet">and</span> analysis are still 'uncharted territory' on the <span class="snippet">social network analysis</span> map. Therefore the new method for the group evolution discovery called GED is proposed in this paper. Additionally, the results of the first experiments on the email based social network together with comparison with two other methods of group evolution discovery are presented.
										
						                	
						                	
						                	
						                			
						             				
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										Who are the Players? Finding <span class="snippet">and</span> Characterizing Stakeholders in Social Networks
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						Costa, C.C.; da Cunha, P.R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5428222">
			                	<br>System Sciences (HICSS), 2010 43rd Hawaii International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/HICSS.2010.443" target="blank">
									10.1109/HICSS.2010.443
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						 <br>Publication Year: 2010
							
										
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										Technological advances <span class="snippet">and</span> the generalized use of the Internet provide the necessary conditions to exploit social networks. These inter-organizational systems involve several players, with different influences, roles <span class="snippet">and</span> interests. To design social networks it is critical to identify its stakeholders. Neglecting their presence can hide network threats that can compromise its success, as well as the development or acceptance of its supporting information system. We present an approach for carrying out stakeholder identification considering the specific demands of social networks. We sought inspiration in stakeholder identification criteria for inter-organizational environments, in <span class="snippet">social network analysis</span>, in business model theories, <span class="snippet">and</span> in social theories. The result was an iterative approach that systematically helps refining the stakeholder set, their roles, <span class="snippet">and</span> the social network context. We illustrate the use of our proposal through its application to Facebook.
										
						                	
						                	
						                	
						                			
						             				
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										<span class="snippet">Social network analysis</span> in environmental management of paraxyleneproject
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						Meng-Hua Song 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6338447">
			                	<br>Machine Learning <span class="snippet">and</span> Cybernetics (ICMLC), 2012 International Conference on
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						Volume:	2					
					
						
						
						
					
					
										 	
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICMLC.2012.6359000" target="blank">
									10.1109/ICMLC.2012.6359000
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						 <br>Publication Year: 2012
							
										
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										The public participation mechanism has been widely introduced to environmental management, which is an important topic in the filed of petrochemical projects. Particularly, the choice of participants is a very important issue for public participation. In this paper, we propose a new strategy to solve the problem of choosing public participants in the environmental management of petrochemical projects from the perspective of <span class="snippet">social network analysis</span>. Moreover, by analyzing the case of Dalian PX project, the result proves that our proposed method is feasible <span class="snippet">and</span> effective.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6113166&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding <span class="snippet">and</span> Metric Timelines
									</a>
								
							
						
					
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						Khurana, U.; Viet-An Nguyen; Hsueh-Chien Cheng; Jae-wook Ahn; Xi Chen; Shneiderman, B. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6112285">
			                	<br>Privacy, security, risk <span class="snippet">and</span> trust (passat), 2011 ieee third international conference on <span class="snippet">and</span> 2011 ieee third international conference on social computing (socialcom)
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
					
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/PASSAT/SocialCom.2011.212" target="blank">
									10.1109/PASSAT/SocialCom.2011.212
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										We present Net EvViz, a visualization tool for analysis <span class="snippet">and</span> exploration of a dynamic social network. There are plenty of visual <span class="snippet">social network analysis</span> tools but few provide features for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the code base of the Node XL template for Microsoft Excel, a popular network visualization tool. The key features of this work are (1) The ability of the user to specify <span class="snippet">and</span> edit temporal annotations to the network components in an Excel sheet, (2) See the dynamics of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, <span class="snippet">and</span> (3) Temporal exploration of the network layout using an edge coloring scheme <span class="snippet">and</span> a dynamic Time slider. The objectives of the new features presented in this paper are to let the data analysts, computer scientists <span class="snippet">and</span> others to observe the dynamics or evolution in a network interactively. We presented Net EvViz to five users of Node XL <span class="snippet">and</span> received positive responses.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6298827&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Semantic Network Analysis of Islamist Sources Using Time Slices as Nodes <span class="snippet">and</span> Semantic Similarity as Link Strengths: Some Implications for Propaganda Analysis about Jihad
									</a>
								
							
						
					
				</h3>
				
					
						Danowski, J.A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6298753">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics Conference (EISIC), 2012 European
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/EISIC.2012.53" target="blank">
									10.1109/EISIC.2012.53
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						 <br>Publication Year: 2012
							
										
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										This research analyzes Muslim nation (MN) networks associated with Jihad for the previous two years. We captured all documents from Lexis-Nexis Academic's BBC International Monitoring - which contains translated transcriptions of web pages, broadcasts, newspapers, <span class="snippet">and</span> other content - for each of 47 Muslim nations (MNs) using the search term: jihad <span class="snippet">and</span> MN name. We presented a new kind of semantic network time series analysis of this text. Unlike most semantic network analysis, our nodes were time segments, not words. The link strengths were similarity scores of time nodes across 779,192 word pairs. The time nodes were 105 weekly intervals. We created a two-mode matrix. Columns were the frequencies of time slices' word pairs, appearing in a three-word window. Matrix rows were three-quarters of a million word pairs extracted from the aggregate two-year text file. We converted this two-mode matrix to a one-mode matrix by computing the similarity of each pair of time slices across the rows of word pairs. This resulted in a one-mode network of 105 by 105 time units. Pearson correlations were the similarity coefficients. We conducted <span class="snippet">social network analysis</span> of the time nodes to find the most central ones. Highly central nodes lie more often on the shortest paths between all pairs of time nodes. They therefore contain in their internal lists of highest frequency word pairs the main themes across the two-years of text. The method is highly automated <span class="snippet">and</span> efficient. In this case only three central nodes provided the basis for an analyst's interpretations of main propaganda themes.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231827&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										An Application of Multicriteria Weighted Graph Similarity Method to Social Networks Analyzing
									</a>
								
							
						
					
				</h3>
				
					
						Tarapata, Z.; Kasprzyk, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.33" target="blank">
									10.1109/ASONAM.2009.33
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										In the paper a concept of multicriteria weighted graphs similarity (MWGSP) method <span class="snippet">and</span> its application to examine some properties of social networks is considered. The approach extends known approaches based on the graph similarity with two features: (1) the similarity is calculated as structural <span class="snippet">and</span> non-structural (quantitative) in weighted graph, (2) choice of the most similar graph (subgraph) to graph (subgraph) representing examined objects is based on multicriteria decision. We test our similarity measures on email network for which the expected results are known, <span class="snippet">and</span> on the terrorist net that prepared <span class="snippet">and</span> executed September 11, 2001 attacks.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231879&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Local Community Identification in Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Jiyang Chen; Zaiane, O.; Goebel, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.14" target="blank">
									10.1109/ASONAM.2009.14
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									 Cited by 7
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
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										There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, <span class="snippet">and</span> then optimizes the community hierarchy. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility <span class="snippet">and</span> effectiveness of our approach.
										
						                	
						                	
						                	
						                			
						             				
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										Social Psychology Testing Platform Leveraging Facebook <span class="snippet">and</span> SNA Techniques
									</a>
								
							
						
					
				</h3>
				
					
						Biancini, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6387876">
			                	<br>Signal Image Technology <span class="snippet">and</span> Internet Based Systems (SITIS), 2012 Eighth International Conference on
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967026">
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Transportation</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SITIS.2012.110" target="blank">
									10.1109/SITIS.2012.110
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										In psychology, testing is largely used in order to assess mental constructs, such as cognitive <span class="snippet">and</span> emotional functioning of individuals. Tests permit researchers to easily reach a large population of subjects. Tests introduce also quantitative aspects to the psychological introspection mechanisms. When conducting sociological researches, it is very important to collect data about subjects' participation <span class="snippet">and</span> relationship with his social environment. In many studies, Facebook data has been used as a reliable approximation of the social behavior of subjects. This article presents a Facebook application designed to provide additional social information to the tests implemented on Open Sesame platform, an open source platform which provides researches with an advanced <span class="snippet">and</span> easy to use experiment builder to support their researches. The application presented computes sociological indexes starting from Facebook data <span class="snippet">and</span> using SNA techniques. It is intended to be an extensible <span class="snippet">and</span> easy to expand solution to ease the leveraging of Facebook data to perform sociological testing <span class="snippet">and</span> collect social data. The SNA indexes are meant to integrate the data coming from classical test execution with the Open Sesame platform. The new indexes will enrich test data with social information about the subjects <span class="snippet">and</span> will permit to perform causal relation researches or verify correlations among different variables.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5169251&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										<span class="snippet">Social Network Analysis</span> of Network Communities
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						Qiang Yan; Min Tang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5169214">
			                	<br>Mobile Business, 2009. ICMB 2009. Eighth International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ICMB.2009.34" target="blank">
									10.1109/ICMB.2009.34
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						 <br>Publication Year: 2009
							
										
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										The development of blog presents characters of social networks. According to the analyses of networks of friends <span class="snippet">and</span> visitors in blog communities, the density <span class="snippet">and</span> centrality are very low. Blog doesnpsilat present apparent trends of centrality. There are many isolated members in blog communities. So currently blog is still merely a platform for Web logging. It hasnpsilat been a media for information dissemination.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5698523&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Using <span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Hierarchical Clustering to Identify Groups in Wikis
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				</h3>
				
					
						Barth, F.J. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5692852">
			                	<br>Collaborative Systems II - Simposio Brasileiro de Sistemas Colaborativos (SBSC-II), 2010 Brazilian Symposium of
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/SBSC-II.2010.11" target="blank">
									10.1109/SBSC-II.2010.11
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										This article explores hierarchical clustering <span class="snippet">and</span> graph analysis for detection groups in Wikis. Both approaches are explored in this work using historical information about Wiki pages. The results shows that this type of analysis can be used to identify people <span class="snippet">and</span> groups with similar interests <span class="snippet">and</span> abilities.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5992293&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										A Study on an Automatic Early-Alert Mechanism for a Tag-based Learning Environment: Development of a Teaching Support Platform Based on a Tag-based Knowledge Acquisition Approach
									</a>
								
							
						
					
				</h3>
				
					
						Jun Ming Chen; Ying Ying Chen; Sun, Y.S.; Meng Chang Chen 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5991609">
			                	<br>Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
						   	;
						   
						
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICALT.2011.31" target="blank">
									10.1109/ICALT.2011.31
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										Reading exercises are critical for developing strong reading comprehension. With the advent of Web 2.0 technology, researchers have attempted to use Web 2.0 tools to develop adaptive <span class="snippet">and</span> cooperative learning environments. Moreover, previous research has demonstrated that the use of social tagging technologies allows teachers to empower learners <span class="snippet">and</span> create exciting new learning opportunities. However, in building a teaching support system, one of the major difficulties is the lack of a mechanism to assist teachers in monitoring running activities <span class="snippet">and</span> student progress, such that constructive suggestions can be given to the students, <span class="snippet">and</span> tutoring strategies can be improved accordingly. To cope with this problem, this paper proposes an automatic tag-based early-alert mechanism (TEA). Moreover, an application to the development of a tag-based collaborative reading learning system has depicted the superiority of our approach.
										
						                	
						                	
						                	
						                			
						             				
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										A combined semantic <span class="snippet">social network analysis</span> framework to integrate social media data
									</a>
								
							
						
					
				</h3>
				
					
						Pankong, N.; Prakancharoen, S.; Buranarach, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6275518">
			                	<br>Knowledge <span class="snippet">and</span> Smart Technology (KST), 2012 4th International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/KST.2012.6287736" target="blank">
									10.1109/KST.2012.6287736
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2012
							
										
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										The technology of web 2.0 especially social networking has changed people's behavior, attitudes, interactions, <span class="snippet">and</span> relationships. User activities on social networking sites have created both explicit <span class="snippet">and</span> implicit relationship. The relations can be distinguished for the sites with symmetric <span class="snippet">and</span> asymmetric relationship types. In this paper, we propose a framework for semantic <span class="snippet">social network analysis</span> for recommending user member groups on social media sites that combines explicit <span class="snippet">and</span> implicit relationship. The framework utilizes semantic technology, i.e. ontology <span class="snippet">and</span> RDF, to integrate the resulted data from different sites. Moreover, a presentation of a ontology design for user <span class="snippet">and</span> activities referring to social network sites. An example of the integrated user profile is demonstrated using FOAF vocabulary.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5231866&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Harnessing Wisdom of the Crowds Dynamics for Time-Dependent Reputation <span class="snippet">and</span> Ranking
									</a>
								
							
						
					
				</h3>
				
					
						Daly, E.M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.69" target="blank">
									10.1109/ASONAM.2009.69
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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							267	- 272
													
													
						
						
						 
						
					
					
					
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									 Cited by 1
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									<p>
										The ldquowisdom of the crowdsrdquo is a concept used to describe the utility of harnessing group behaviour, where user opinion evolves over time <span class="snippet">and</span> the opinion of the masses collectively demonstrates wisdom. Web 2.0 is a new medium where users are not just consumers, but are also contributors. By contributing content to the system, users become part of the network <span class="snippet">and</span> relationships between users <span class="snippet">and</span> content can be derived. Example applications are collaborative bookmarking networks such as delicious <span class="snippet">and</span> file sharing applications such as YouTube <span class="snippet">and</span> Flickr. These networks rely on user contributed content, described <span class="snippet">and</span> classified using tags. The wealth of user generated content can be hard to navigate <span class="snippet">and</span> search due to difficulties in comparing documents with similar tags <span class="snippet">and</span> the application of traditional information retrieval scoring techniques are limited. Evaluating the time evolving interests of users may be used to derive quality of content. In this paper, we propose a technique to rank documents based on reputation. The reputation is a combination of the number of bookmarkers, the reputation of the bookmarking user <span class="snippet">and</span> the time dynamics of the document. Experimental results <span class="snippet">and</span> analysis are presented on a large collaborative IBM bookmarking network called Dogear.
										
						                	
						                	
						                	
						                			
						             				
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				<h3>           
					
						
						
							
								
								
										
									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6121119&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Construction of Intelligent Information Service Platform for Scientific Research
									</a>
								
							
						
					
				</h3>
				
					
						Hui Du; Yongjie Dang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6120133">
			                	<br>Business Intelligence <span class="snippet">and</span> Financial Engineering (BIFE), 2011 Fourth International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/BIFE.2011.41" target="blank">
									10.1109/BIFE.2011.41
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							194	- 198
													
													
						
						
						 
						
					
					
					
			
						
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										This paper set up a web-based intelligent information service platform of scientific research, achieved automatic information collection, unscrambling, analysis <span class="snippet">and</span> excavation based on a variety of academic database at home <span class="snippet">and</span> abroad, implemented the co-word analysis <span class="snippet">and</span> co-citation analysis of the joined literatures, <span class="snippet">and</span> subject knowledge structure of the targeted research field was revealed to some extent. The method of <span class="snippet">social network analysis</span> was used to mine the network structure of cooperative relationship <span class="snippet">and</span> the potential cooperative relationship between the researchers, then the core research group of the targeted field could be found. We used the indicators of bibliometrics <span class="snippet">and</span> <span class="snippet">social network analysis</span> to evaluate the expert in the specific field, <span class="snippet">and</span> can provide information services <span class="snippet">and</span> decision-making support to the scientific researchers <span class="snippet">and</span> the department of research management.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6085937&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Entropy based community detection in augmented social networks
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				</h3>
				
					
						Cruz, J.D.; Bothorel, C.; Poulet, F. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6082090">
			                	<br>Computational Aspects of Social Networks (CASoN), 2011 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/CASON.2011.6085937" target="blank">
									10.1109/CASON.2011.6085937
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										<span class="snippet">Social network analysis</span> has become a major subject in recent times, bringing also several challenges in the computer science field. One aspect of the <span class="snippet">social network analysis</span> is the community detection problem, which can be seen as a graph clustering problem. However, social networks are more than a graph, they have an interesting amount of information derived from its social aspect, such as profile information, content sharing <span class="snippet">and</span> annotations, among others. Most of the community detection algorithms use only the structure of the network, i.e., the graph. In this paper we propose a new method which uses the semantic information along with the network structure in the community detection process. Thus, our method combines an algorithm for optimizing modularity <span class="snippet">and</span> an entropy-based data clustering algorithm, which tries to find a partition with low entropy <span class="snippet">and</span> keeping in mind the modularity.
										
						                	
						                	
						                	
						                			
						             				
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										Awareness of Trends in e-Democratic Discussions
									</a>
								
							
						
					
				</h3>
				
					
						Tavares, R.L.; Pimentel, M.; de Araujo, R.M.; Cappelli, C. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5692851">
			                	<br>Collaborative Systems - Simposio Brasileiro de Sistemas Colaborativos (SBSC), 2010 Brazilian Symposium of
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/SBSC.2010.19" target="blank">
									10.1109/SBSC.2010.19
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										This article presents a proposal to the visualization of tendencies in democratic discussions with social networks. In this discussion context, it is hard to follow the discussion <span class="snippet">and</span> to have awareness of others' opinion, due to the volume of messages that are produced <span class="snippet">and</span> the number of participants. This work proposes a process of <span class="snippet">social network analysis</span> of discussions <span class="snippet">and</span> presents a preliminary specification of a system to support it.
										
						                	
						                	
						                	
						                			
						             				
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										Mining Proximal Social Network Intelligence for Quality Decision Support
									</a>
								
							
						
					
				</h3>
				
					
						Yuan-Chu Hwang 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.22" target="blank">
									10.1109/ASONAM.2009.22
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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									<p>
										The concepts of proximity have been utilized for exploring both psychological <span class="snippet">and</span> geographical incentives for users within social networks to collaborate with others for mutual goals. The massive information does not facilitate quality decision support. In this paper, we focus on mining the proximal social network intelligence for quality decision support. The utilization of investigating both the context <span class="snippet">and</span> the content of the application domain from social network relationships would highly improve the information quality for better decisions. Mining proximal social network intelligence from both context <span class="snippet">and</span> content enable quality decision making. We illustrate a case of leisure recommendation e-service for bicycle exercise entertainment in Taiwan. We introduce the proximity e-service as well as its theoretical support.The most recent personalized experience according to its context provides remarkable perceptual data from unique information sources. Moreover, the social network relationships extend the power of the unique perceptual information to converge as the collective social network intelligence.
										
						                	
						                	
						                	
						                			
						             				
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										How personal fitness data can be re-used by smart cities
									</a>
								
							
						
					
				</h3>
				
					
						Clarke, A.; Steele, R. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6135451">
			                	<br>Intelligent Sensors, Sensor Networks <span class="snippet">and</span> Information Processing (ISSNIP), 2011 Seventh International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967043">
								Geoscience</a>
						   
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								Nuclear Engineering</a>
						   
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								Photonics &amp; Electro-Optics</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ISSNIP.2011.6146582" target="blank">
									10.1109/ISSNIP.2011.6146582
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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							395	- 400
													
													
						
						
						 
						
					
					
					
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									 Cited by 1
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										The growing trend to use mobile devices <span class="snippet">and</span> applications to collect data relating to fitness activities has resulted in large amounts of sensor data being generated. Further, in some cases the fitness data is shared over social networks. This collected data has potential uses in a number of fields including: public health <span class="snippet">and</span> population health data, urban planning, fitness trends analysis, <span class="snippet">social network analysis</span> <span class="snippet">and</span> personalization of health information. As the motivation for creating this sensor data already exists for the individual in relation to fitness benefits <span class="snippet">and</span> health monitoring, this type of participatory sensing approach has a lower barrier to entry. However, there is currently no structured approach to collect <span class="snippet">and</span> re-use this sensor data. This paper describes the distinct types of fitness sensor applications; conceptual architecture for data collection <span class="snippet">and</span> aggregation <span class="snippet">and</span> the steps <span class="snippet">and</span> developments that would improve the quality <span class="snippet">and</span> usability of data collected. Further, the types of secondary uses for smart cities of this collected data are explored.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1625404&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Notice of Violation of IEEE Publication Principles<br>Practical approaches for analysis, visualization <span class="snippet">and</span> destabilizing terrorist networks
									</a>
								
							
						
					
				</h3>
				
					
						Memon, N.; Larsen, H.L. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10823">
			                	<br>Availability, Reliability <span class="snippet">and</span> Security, 2006. ARES 2006. The First International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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								Power, Energy, &amp; Industry Applications</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ARES.2006.95" target="blank">
									10.1109/ARES.2006.95
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2006
							
										
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										Notice of Violation of IEEE Publication Principles<br><br> "Practical Approaches for Analysis, Visualization <span class="snippet">and</span> Destabilizing Terrorist Networks" <br> by Nasrullah Memon <span class="snippet">and</span> Henrik Legind Larsen<br> in Proceedings of the First International Conference on Availability, Reliability <span class="snippet">and</span> Security (ARES), April 2006<br><br> After careful <span class="snippet">and</span> considered review of the content <span class="snippet">and</span> authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. <br><br> This paper has copied portions of text from the sources cited below. The lead author, Nasrullah Memon, was found to be solely responsible for the violation. The original text was copied without attribution (including appropriate references to the original author(s) <span class="snippet">and</span>/or paper title) <span class="snippet">and</span> without permission. <br><br> "Untangling Criminal Networks: A Case Study"<br> by Jennifer Xu, Hsinchun Chen<br> Proceedings of the First NSF/NIJ Symposium Intelligence <span class="snippet">and</span> Security Informatics, ISI, June 2003<br><br> "The Exploratory Construction of Database Views" <br> by M.N. Smith, P.J.H. King<br> Research Report BBKCS, School of Computer Science <span class="snippet">and</span> Information Systems, Birbeck College, University of London, 2002<br><br>Traditionally most of the literature in <span class="snippet">social network analysis</span> (SNA) has focused on networks of individuals. Although SNA is not conventionally considered as a data mining technique, it is especially suitable for mining a large volume of association data to discover hidden structural patterns in terrorist networks. After September 11 attacks, SNA has increasingly been used to study terrorist networks. As these covert networks share some features with conventional networks, they are harder to identify because they mask their transactions. The most complicating factor is that terrorist networks are often embedded in a much larger population (i.e., adversaries have links with both covert <span class="snippet">and</span> innocent individuals). Hence, it is desirable to ha- e tools to correctly classify individuals in covert networks so that the resources for isolating them will be used more efficiently. This paper uses centrality measures from complex networks to discuss how to destabilize adversary networks. We propose newly introduced algorithms for constructing hierarchy of the covert networks, so that investigators can view the structure of the ad hoc networks/atypical organizations, in order to destabilize the adversaries. The algorithms are also demonstrated by using publicly available dataset. Moreover we also demonstrate techniques for filtering graphs (networks)/detecting particular cells in adversary networks using a fictitious dataset.
										
						                	
						                	
						                	
						                			
						             				
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										Taiwan Academic Network Discussion via Social Networks Analysis Perspective
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						Chen-Shu Wang; I-Hsien Ting; Yu-Chieh Li 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2011.99" target="blank">
									10.1109/ASONAM.2011.99
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						 <br>Publication Year: 2011
							
										
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										Academic network (AN) is quite different network <span class="snippet">and</span> hard to acquire relative information for inexperience researchers. To realize AN is an important but difficult work because of the constituents of AN are complicated <span class="snippet">and</span> dynamic variation. In this research, two Taiwan academic networks are established from the perspectives of academic conference topic <span class="snippet">and</span> participant committee. Each academic conference is regarded as an AN node to establish the conference academic network. In addition, there are two connection types between AN nodes, including: the relationship among CFP topics (academic conference topic perspective) <span class="snippet">and</span> the relationship between committee member (participant committee perspective). Finally, 15 IT/IM relative target conferences in Taiwan 2009 are analyzed via <span class="snippet">social network analysis</span> methodology. According to experiment result, the values of degree <span class="snippet">and</span> betweeness reveal some interesting findings, such as TANET, OOTA <span class="snippet">and</span> IMP are top three important bridge conferences in Taiwan. Additionally, as the cluster analyzed result shown, academic network somehow represents domain expertise because of the committee are grouped according to their research domains. These finding is consistence with the reality academic network in Taiwan <span class="snippet">and</span> helpful information for Taiwan academic network understanding.
										
						                	
						                	
						                	
						                			
						             				
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										Detection of Overlapping Communities in Dynamical Social Networks
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						Cazabet, R.; Amblard, F.; Hanachi, C. 
					
					
						
						
							            
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			                	<br>Social Computing (SocialCom), 2010 IEEE Second International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/SocialCom.2010.51" target="blank">
									10.1109/SocialCom.2010.51
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						 <br>Publication Year: 2010
							
										
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										Community detection on networks is a well-known problem encountered in many fields, for which the existing algorithms are inefficient 1) at capturing overlaps in-between communities, 2) at detecting communities having disparities in size <span class="snippet">and</span> density 3) at taking into account the networks' dynamics. In this paper, we propose a new algorithm (iLCD) for community detection using a radically new approach. Taking into account the dynamics of the network, it is designed for the detection of strongly overlapping communities. We first explain the main principles underlying the iLCD algorithm, introducing the two notions of intrinsic communities <span class="snippet">and</span> longitudinal detection, <span class="snippet">and</span> detail the algorithm. Then, we illustrate its efficiency in the case of a citation network, <span class="snippet">and</span> then compare it with existing most efficient algorithms using a standard generator of community-based networks, the LFR benchmark.
										
						                	
						                	
						                	
						                			
						             				
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										Social Influence Models Based on Starbucks Networks
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						Minkyoung Kim; Byoung-Tak Zhang; June-Sup Lee 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5176085">
			                	<br>Computational Aspects of Social Networks, 2009. CASON '09. International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/CASoN.2009.26" target="blank">
									10.1109/CASoN.2009.26
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										Starbucks coffee shops have been spread rapidly <span class="snippet">and</span> widely all over the world, which implies that there may be diffusive powers among them <span class="snippet">and</span> thus can be represented as social networks. In particular, the spreading speed of Starbuck Korea was at record levels. In this paper, we constructed social networks using the information about Starbuck Korea (ex. latitude <span class="snippet">and</span> longitude of each Starbucks store in Korea, the opening date of them, opening orders of them, etc.) <span class="snippet">and</span> evaluated influence scores of each store to measure the spreading power of Starbucks in Korea. Here, we proposed two network evaluation models, Dynamic Influence Model <span class="snippet">and</span> Static Influence Model. Through these models, we can represent location based social networks <span class="snippet">and</span> evaluate each node's diffusive power for expanding the size of networks <span class="snippet">and</span> for spreading coverage all over the network.
										
						                	
						                	
						                	
						                			
						             				
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										Finding Strong Groups of Friends among Friends in Social Networks
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						Cameron, J.J.; Leung, C.K.-S.; Tanbeer, S.K. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6118336">
			                	<br>Dependable, Autonomic <span class="snippet">and</span> Secure Computing (DASC), 2011 IEEE Ninth International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967042">
								Power, Energy, &amp; Industry Applications</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/DASC.2011.141" target="blank">
									10.1109/DASC.2011.141
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						 <br>Publication Year: 2011
							
										
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							<div style="width: auto; overflow: visible; display: none;" class="abstract RevealContent" id="abstract-1">
									<p>
										Over the past few years, the rapid growth <span class="snippet">and</span> the exponential use of social digital media has led to an increase in popularity of social networks <span class="snippet">and</span> the emergence of social computing. In general, social networks are structures made of social entities (e.g., individuals) that are linked by some specific types of interdependency such as friendship. Most users of social media (e.g., Face book, Google+, Linked In, My Space, Twitter) have many linkages in terms of friends, connections, <span class="snippet">and</span>/or followers. Among all these linkages, some of them are more important than another. For instance, some friends of a user may be casual ones who acquaintances met him at some points in time, whereas some others may be friends that care about him in such a way that they frequently post on his wall, view his updated profile, send him messages, invite him for events, <span class="snippet">and</span>/or follow his tweets. In this paper, we apply data mining techniques to social networks to help users of the social digital media to distinguish these important friends from a large number of friends in their social networks.
										
						                	
						                	
						                	
						                			
						             				
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										SQL Privacy Model for Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Majedi, M.; Ghazinour, K.; Chinaei, A.H.; Barker, K. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5231771">
			                	<br><span class="snippet">Social Network Analysis</span> <span class="snippet">and</span> Mining, 2009. ASONAM '09. International Conference on Advances in
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2009.60" target="blank">
									10.1109/ASONAM.2009.60
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										This is a preliminary work to extend SQL to support user privacy in social networks. The proposal is to extend the data definition <span class="snippet">and</span> data manipulation languages to capture privacy-preserved mandatory <span class="snippet">and</span> discretionary access controls, respectively. Here, we focus on common user privacy requirements, such as purpose, generalization, <span class="snippet">and</span> retention, used by social networks desiring to support privacy. Hence, each user can discretionarily control the set of privileges over the view representing their profile. We plan to support the extended language with underlying catalogues, algorithms, <span class="snippet">and</span> prototypes. The objective is to develop a low-cost mechanism to preserve privacy in databases,with applications in social networks, e-health, e-business, e-government, etc.
										
						                	
						                	
						                	
						                			
						             				
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										SPOT 1.0: Scoring Suspicious Profiles on Twitter
									</a>
								
							
						
					
				</h3>
				
					
						Perez, C.; Lemercier, M.; Birregah, B.; Corpel, A. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5992553">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2011 International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								<a href="http://dx.doi.org/10.1109/ASONAM.2011.63" target="blank">
									10.1109/ASONAM.2011.63
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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										Everyday more than fifty million messages are generated by about two hundred million profiles on Twitter. Some users attempt to exploit the success of this micro logging platform <span class="snippet">and</span> its relative freedom to perform malicious actions that can lead to identity or data theft. This work aims to propose a framework to assess suspicious behavior on Twitter. We present a tool developed for Scoring Suspicious Profiles On Twitter (SPOT 1.0) through a three-dimensional indicator that involves the degree of aggressiveness, the visibility <span class="snippet">and</span> the level of danger.
										
						                	
						                	
						                	
						                			
						             				
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										Social Interactions Representation as Users Behavioral Contingencies <span class="snippet">and</span> Evaluation in Social Networks
									</a>
								
							
						
					
				</h3>
				
					
						Gomes, A.K.; da Graca C Pimentel, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6059526">
			                	<br>Semantic Computing (ICSC), 2011 Fifth IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ICSC.2011.103" target="blank">
									10.1109/ICSC.2011.103
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										In <span class="snippet">social network analysis</span>, models for the representation of user's interactions do not explain which actions are performed during social interactions, <span class="snippet">and</span> which types of media are used in the interactions. We present a novel technique for the representation of social interactions as users' behavioral contingencies in the form of if-then rules, <span class="snippet">and</span> for the evaluation of the contingencies using data mining procedures. We present the results of applying our technique in a group of Facebook users, identifying the social interactions in which users were more involved the most.
										
						                	
						                	
						                	
						                			
						             				
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										<span class="snippet">Social network analysis</span> plugin (SNAP) for mesh networks
									</a>
								
							
						
					
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						Nanda, S.; Kotz, D. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5770420">
			                	<br>Wireless Communications <span class="snippet">and</span> Networking Conference (WCNC), 2011 IEEE
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/WCNC.2011.5779252" target="blank">
									10.1109/WCNC.2011.5779252
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										In a network, bridging nodes are those nodes that from a topological perspective, are strategically located between highly connected regions of nodes. Thus, they have high values of the Bridging Centrality (BC) metric. We recently introduced the Localized Bridging Centrality (LBC) metric, which can identify such nodes via distributed computation, yet has an accuracy equal to that of the centralized BC metric. The LBC <span class="snippet">and</span> BC metrics are based on the <span class="snippet">Social Network Analysis</span> (SNA) metric “betweenness centrality”. We now introduce a new SNA metric that is more suitable for use in wireless mesh networks: the Localized Load-aware Bridging Centrality (LLBC) metric. The LLBC metric improves upon LBC by detecting critical bridging nodes while taking into account the actual traffic flows present in a mesh network. We only use local information from surrounding nodes to compute the LLBC metric, thus our LLBC metric is designed for scalable distributed computation <span class="snippet">and</span> distributed network analysis. We developed the SNA Plugin (SNAP) for the Optimized Link State Routing (OLSR) protocol to study the potential use of LBC <span class="snippet">and</span> LLBC in improving multicast communications. We present some promising initial results for SNAP from real <span class="snippet">and</span> emulated mesh networks. SNAP is open source <span class="snippet">and</span> free for academic use.
										
						                	
						                	
						                	
						                			
						             				
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						Tomasev, N.; Mladenic, D. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5174538">
			                	<br>Information Technology Interfaces, 2009. ITI '09. Proceedings of the ITI 2009 31st International Conference on
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					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ITI.2009.5196135" target="blank">
									10.1109/ITI.2009.5196135
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2009
							
										
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										This paper presents experiments with applying <span class="snippet">social network analysis</span> on data about editing of semantic media wiki. As a number of users are editing the same wiki pages, one can view them as a social network of people interacting via wiki pages. We propose representation of the wiki editing log files as a graph either of users that are connected if they are editing the same pages or of pages that are connected if edited by the same users. We apply <span class="snippet">social network analysis</span> on such graphs in order to provide some insights into activity of the editors of semantic wiki pages.
										
						                	
						                	
						                	
						                			
						             				
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										A study on knowledge flow <span class="snippet">and</span> its implications via social networking site
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						Sugathan, S.K.; Azlin, A.R.F.; Goh Kim Nee; Akhir, E.A.P.; Yie, C.Y. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5523828">
			                	<br>Information Technology (ITSim), 2010 International Symposium in
			                </a>
						
							
						
							
												
					
						
					
		       			<br>  
						Volume:	1					
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ITSIM.2010.5561420" target="blank">
									10.1109/ITSIM.2010.5561420
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										This paper consist of exploratory study to investigate the effect of virtual knowledge sharing of a project team in an organization via social networking site. Putting into consideration that Facebook is public domain, a social networking platform of a smaller scale known as Corporate Resource On-Demand &amp; Community Sharing (CRODCS) is developed using open source software. This will allow the chosen platform to be tailor-suited <span class="snippet">and</span> customized to any unit <span class="snippet">and</span> project team preference. This paper employs <span class="snippet">social network analysis</span> techniques using software package Agna 2.1 to map <span class="snippet">and</span> analyze the patterns <span class="snippet">and</span> flow of interactions critical to information <span class="snippet">and</span> knowledge sharing of project discussions via CRODCS <span class="snippet">and</span> gmail. The mapping of the knowledge flow was analyzed <span class="snippet">and</span> compared in both platforms in order to study the effectiveness of knowledge sharing via social networking site.
										
						                	
						                	
						                	
						                			
						             				
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									<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5562773&contentType=Conference+Publications&pageNumber%3D4%26rowsPerPage%3D100%26queryText%3D.QT.social+network+analysis.QT.+AND+online">
										Tracking the Evolution of Communities in Dynamic Social Networks
									</a>
								
							
						
					
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						Greene, D.; Doyle, D.; Cunningham, P. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5562738">
			                	<br>Advances in Social Networks Analysis <span class="snippet">and</span> Mining (ASONAM), 2010 International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967114">
								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ASONAM.2010.17" target="blank">
									10.1109/ASONAM.2010.17
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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									 Cited by 5
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										Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying <span class="snippet">and</span> tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.
										
						                	
						                	
						                	
						                			
						             				
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										A Summary <span class="snippet">and</span> Update of Social Networks <span class="snippet">and</span> Granular Computing
									</a>
								
							
						
					
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						Churn-Jung Liau 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5575317">
			                	<br>Granular Computing (GrC), 2010 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
					 	<br>Topic(s):&nbsp;
					 	
							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294967045">
								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/GrC.2010.29" target="blank">
									10.1109/GrC.2010.29
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2010
							
										
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										In summary, analyzing a social position from a GrC perspective has two major advantages. On the one hand, it extends the application scope of the GrC methodology. On the other hand, we can logically transform relational granulation of social networks into functional granulation; as a result, the well-developed techniques of data table analysis can be applied to social position analysis. Thus, this is an important cross-fertilization of GrC <span class="snippet">and</span> SNA.
										
						                	
						                	
						                	
						                			
						             				
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										Extending the classification of nodes in social networks
									</a>
								
							
						
					
				</h3>
				
					
						Heatherly, R.; Kantarcioglu, M. 
					
					
						
						
							            
			                <a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5975309">
			                	<br>Intelligence <span class="snippet">and</span> Security Informatics (ISI), 2011 IEEE International Conference on
			                </a>
						
							
						
							
												
					
						
					
						
						
						
					
					
										 	
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294961108">
								Bioengineering</a>
						   
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								Components, Circuits, Devices &amp; Systems</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966917">
								General Topics for Engineers (Math, Science &amp; Engineering)</a>
						   
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							<a href="http://ieeexplore.ieee.org/search/searchresult.jsp?refinements=4294966781">
								Signal Processing &amp; Analysis</a>
						   
												
					 
																
					
						<br>Digital Object Identifier:
						
							
							
								<a href="http://dx.doi.org/10.1109/ISI.2011.5984054" target="blank">
									10.1109/ISI.2011.5984054
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										Because of computational concerns, <span class="snippet">social network analysis</span> generally uses only directly connected nodes to perform classification tasks. However, recent research indicates that this method of classification may not consider that nodes in the graph could have different influence over other nodes near them in the graph. It is possible that well-selected nodes may have a stronger importance in a social graph. Here, we analyze methods by which these important nodes may be identified <span class="snippet">and</span> used to improve the classification of nodes within the social graph. We also show the effect of incorporating these important nodes in social network classification.
										
						                	
						                	
						                	
						                			
						             				
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										Social Multimedia Mining: A Social Informatics Perspective
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						Lappas, G. 
					
					
						
						
							            
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			                	<br>Informatics (PCI), 2011 15th Panhellenic Conference on
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					 	<br>Topic(s):&nbsp;
					 	
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								Communication, Networking &amp; Broadcasting</a>
						   
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								Computing &amp; Processing (Hardware/Software)</a>
						   
												
					 
																
					
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								<a href="http://dx.doi.org/10.1109/PCI.2011.59" target="blank">
									10.1109/PCI.2011.59
								</a>
							
						
					
						
					
												  
						 
						 <br>Publication Year: 2011
							
										
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									<p>
										This work conceptualizes <span class="snippet">and</span> introduces the concept of social multimedia mining as a new emerging research area that combines web mining research, multimedia research <span class="snippet">and</span> social media research. New challenges <span class="snippet">and</span> opportunities in research areas of social <span class="snippet">and</span> computational science are discussed under a social informatics perspective.
										
						                	
						                	
						                	
						                			
						             				
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